bims-librar Biomed News
on Biomedical librarianship
Issue of 2026–11–01
forty-four papers selected by
Thomas Krichel, Open Library Society
  1. The factors integrating the learning analytics into academic libraries: Finding from SmartPLS-NCA and cIPMA.
  2. People Overtrust AI-Generated Medical Advice despite Low Accuracy.
  3. BioACE: An automated framework for biomedical answer and citation evaluations.
  4. Study design indexing in transition: a focused comparison of manual NLM indexing versus transformer-based automated models.
  5. BioHarness: Substrate-Aware Evidence Assembly for Biomedical Question Answering across Literature, Knowledge Bases, and Biological Atlases.
  6. RRI-Senti: Transformer-Based Sentiment Analysis of Biomedical Literature for RNA-RNA Interaction Disease Insights.
  7. Large-scale semantic indexing of Spanish biomedical literature using contrastive transfer learning.
  8. SureChEMBL in 2026: an open database of chemical and biomedical annotations from the patent literature.
  9. Efficacy of a training program to support the application of the Guideline Evidence-based Health Information: a randomized controlled trial and process evaluation.
  10. Accuracy, Quality and Readability of Knee Osteoarthritis Information on Social Media (Instagram e TikTok) and Search Platforms (Google e YouTube): A Cross-Sectional Study.
  11. Accuracy, Relevance, and Redundancy of ChatGPT-5.3 References in Open Fracture Management: A Methodological Analysis.
  12. Evaluating the reliability, quality, and readability of AI-generated patient education on hallux valgus: a comparative study of large language models.
  13. Quality and Readability of ChatGPT and Gemini Responses to Patient Questions About Systemic Isotretinoin: Comparative Evaluation Study.
  14. Quality, Transparency, and Readability of Artificial Intelligence Chatbots as Sources of Plagiocephaly Information: A Cross-Sectional Comparative Study.
  15. Quality, Readability, Understandability, and Actionability of AI Chatbots' Responses to Dental Patient Questions: A Comparative Evaluation.
  16. Reliability and readability assessment of appendicitis information from AI language models (ChatGPT, gemini and perplexity) in English and Spanish.
  17. Evaluation of ChatGPT responses to common questions about rTMS: can ChatGPT support patient education about rTMS?
  18. Clinical Safety Thresholds and Decision Risks of Search-Augmented Large Language Models in Acute Pancreatitis: A Guideline-Anchored Evaluation for Surgical Practice.
  19. Comparative evaluation of large language models for esophageal cancer patient education: quality, reliability, readability, understandability, and actionability.
  20. Content and Quality Analysis of Chikungunya Fever-Related Online Information on Social Media: A Cross-Sectional Study.
  21. Four large language models in parent-oriented education for pediatric flexible flatfoot: Quality, readability, hallucination, and reproducibility.
  22. Reliability and readability of five large language models in answering patient-oriented questions about rectal prolapse: a comparative study.
  23. Readability, understandability and actionability of BAUS and EAU patient education materials for common non-oncological urological conditions.
  24. Evaluating the Quality and Reliability of Cervical Spine Isometric Exercise Videos on YouTube for Neck Pain: A Cross-sectional Study.
  25. Quality and Reliability of YouTube Videos on the Periodontal Tunnel Technique and Their Coverage of Procedure-Specific Content: A Cross-Sectional Study.
  26. General information reliability, perceived educational quality, and transparency of YouTube videos on epileptiform discharges: an exploratory cross-sectional study.
  27. Quality and reliability of cancer- and tumor-related videos on YouTube: a scoping review.
  28. Evaluating the quality and reliability of YouTube videos about sepsis.
  29. Three-dimensional evaluation and optimization of breast cancer rehabilitation science popularization videos on mainstream video platforms: a cross-sectional study in Mainland China.
  30. Quality and content assessment of short videos as sources of health information about gastroscopy in China: a cross-sectional study.
  31. Quality, reliability, and stigmatizing content of sexual and gender minority health videos on Chinese short-form video platforms.
  32. Quality of Douyin videos on childhood asthma: A cross-sectional content analysis.
  33. Quality and Reliability of Neck Wrinkle-Related Short Videos on TikTok and Bilibili: A Cross-Sectional Study.
  34. The quality and reliability of short videos about myocarditis on Bilibili and TikTok: cross-sectional study.
  35. TikTok Videos on the Medial Patellofemoral Ligament Have Low Educational Quality and Reliability.
  36. The quality and reliability of short videos about Marfan syndrome on Bilibili and Douyin: a cross-sectional study.
  37. Quality and reliability of sarcopenia-related short videos on Douyin: A cross-sectional content analysis.
  38. Creator Type, Content Format, and Engagement Among Highly Liked Cancer-Related TikTok Search Results: A Cross-Sectional Content Analysis.
  39. Quality, source transparency, information reliability, and domain coverage of short video-based information on feeding guidance for people with dysphagia: a cross-sectional study on 4 major Chinese social media platforms.
  40. Quality and engagement of #chiropractic content on TikTok: A DISCERN-based cross-sectional analysis.
  41. Frequency of Internet Use and Cancer Information Seeking Among U.S. Adults: A Nationally Representative Analysis of the 2024 Health Information National Trends Survey (HINTS-7).
  42. Cancer Information Burden Among US Cancer Survivors Using Health Information National Trends Survey 2022 and 2024: Cross-Sectional Study.
  43. Public Health Information Seeking Behaviours Among Young Adults With Migrant Backgrounds in Australia: A Cross-Sectional Study.
  44. Selective Reception of the Literature.
  1. Acta Psychol (Amst). 2026 Oct 26. pii: S0001-6918(26)01864-0. [Epub ahead of print]271 108062
    The factors integrating the learning analytics into academic libraries: Finding from SmartPLS-NCA and cIPMA.
    Khurshid Ahmad, Aref Alsehaimi, Kashif Ali, Nain Tara.
      With the rapid advancement of the digital environment, the learning behaviors of digital natives have undergone significant transformation, and academic libraries are increasingly expected to draw on learner-related data to inform their services. This study examines the factors associated with librarians' behavioral intention to use, and their actual use of, learning analytics in academic libraries. The research is anchored in the Unified Theory of Acceptance and Use of Technology (UTAUT), which was extended with three additional structural paths (performance expectancy → effort expectancy, effort expectancy → social influence, and social influence → facilitating conditions). A cross-sectional questionnaire was analyzed with PLS-SEM in Smart-PLS 4, supplemented by Necessary Condition Analysis (NCA) and Combined Importance-Performance Map Analysis (cIPMA). Performance expectancy (β = 0.467) and effort expectancy (β = 0.199) were positively associated with behavioral intention, whereas the social-influence path was not statistically significant (β = 0.118, p = 0.216). Behavioral intention (β = 0.565) and facilitating conditions (β = 0.143) were associated with reported use. The model accounted for 36.8% of the variance in behavioral intention and 37.7% in use behavior. According to the NCA, performance expectancy was the only antecedent that met the necessity criterion for behavioral intention. Given the cross-sectional design and the composition of the sample, the results describe associations among the constructs for this group of respondents and should not be read as causal effects or generalized to all academic librarians. The study extends UTAUT to the context of learning analytics in academic libraries and offers preliminary, evidence-informed insights for library administrators and policymakers.
    Keywords:  Academic libraries; Higher education institutions; Learning analytics; Structural equation modeling; UTAUT
    DOI:  https://doi.org/10.1016/j.actpsy.2026.108062
  2. NEJM AI. 2025 Jun;2(6): AIoa2300015
    People Overtrust AI-Generated Medical Advice despite Low Accuracy.
    Shruthi Shekar, Pat Pataranutaporn, Chethan Sarabu, Guillermo A Cecchi, Pattie Maes.
       BACKGROUND: This article presents a comprehensive analysis of how artificial intelligence (AI)-generated medical responses are perceived and evaluated by nonexperts.
    METHODS: We conducted a study in which a total of 300 participants gave evaluations for medical responses that were either written by a medical doctor on an online health care platform or generated by a large language model and labeled by physicians as having high accuracy or low accuracy.
    RESULTS: Results showed that participants could not effectively distinguish between AI-generated responses and doctors' responses and demonstrated a preference for AI-generated responses, rating high-accuracy AI-generated responses as significantly more valid, trustworthy, and complete/satisfactory. Low-accuracy AI-generated responses on average performed very similarly to doctors' responses. Participants not only found these low-accuracy AI-generated responses to be valid, trustworthy, and complete/satisfactory, but also indicated a high tendency to follow the potentially harmful medical advice and incorrectly seek unnecessary medical attention as a result of the response provided. This problematic reaction was comparable with, if not stronger than, the reaction they displayed toward doctors' responses. Both experts and nonexperts exhibited bias, finding AI-generated responses to be more thorough and accurate than doctors' responses but still valuing the involvement of a doctor in the delivery of their medical advice.
    CONCLUSIONS: The increased trust placed in inaccurate or inappropriate AI-generated medical advice can lead to misdiagnosis and harmful consequences for individuals seeking help. Further, participants were more trusting of high-accuracy AI-generated responses when told they were given by a doctor, and experts rated AI-generated responses significantly higher when the source of the response was unknown. Ultimately, AI systems should be implemented in collaboration with medical professionals when used for the delivery of medical advice in order to prevent misinformation while reaping the benefits of such cutting-edge technology.
    DOI:  https://doi.org/10.1056/AIoa2300015
  3. J Biomed Inform. 2026 Oct 26. pii: S1532-0464(26)00137-1. [Epub ahead of print] 105113
    BioACE: An automated framework for biomedical answer and citation evaluations.
    Deepak Gupta, Davis Bartels, Dina Demner-Fushman.
       OBJECTIVE: Large Language Models (LLMs) are increasingly used to generate answers to medical questions; however, automated evaluation of the completeness and correctness of the answers remains a challenge, which presents a bottleneck for the development and deployment of question answering systems that are needed to address medical information needs of patients and clinicians. Our goal is to develop an automated evaluation framework for rapid testing of different question answering approaches.
    METHODS: We propose BioACE, an automated framework for the evaluation of completeness and correctness of medical answers and the evidence provided to support the facts stated in the answers. We compare BioACE to multiple existing approaches that evaluate the quality of an answer and the evidence provided to support the generated answers, such as natural language inference (NLI) and pre-trained and large language models (PLMs and LLMs).
    RESULTS: The Llama-3.3-70B-Instruct (zero-shot) model achieved the highest correlation with human judgment in evaluating different aspects of completeness of the answers. Among the evaluated PLM- and LLM-based models, RoBERTaLarge achieved the best overall performance for answer correctness, while BERTLarge also performed competitively and obtained the best results in fully automated classification settings.
    CONCLUSION: The Llama-3.3-70B-Instruct model can be used to evaluate answer completeness during system development. BERTLarge can be used for answer-correctness evaluation without requiring human document-relevance assessment. While our approach to the evaluation of the evidence provided to support the answers outperforms the existing approaches, it still leaves ample room for improvement, demonstrating the complexity of fact attribution. The BioACE (https://github.com/deepaknlp/BioACE/) framework provides a means for rapid evaluation of medical answers, as well as reliable ranking of question answering systems during development. The existing automated evaluation approaches do not yet allow for system deployment without human review.
    Keywords:  Answer attribution; Answer evaluation; Biomedical question answering; Citation evaluation
    DOI:  https://doi.org/10.1016/j.jbi.2026.105113
  4. JAMIA Open. 2026 Oct;9(5): ooag197
    Study design indexing in transition: a focused comparison of manual NLM indexing versus transformer-based automated models.
    Puranjani Das, Jodi Schneider, Evan Mayo-Wilson, Halil Kilicoglu, Joe D Menke, Dongin Nam, Kiran Ninan, Jean-Pierre Oberste, Ang Michael Troy, Xiangji Ying, Arthur W Holt, Neil R Smalheiser.
       Objectives: We sought to evaluate the accuracy and suitability of a transformer-based model (TM) for indexing clinical study designs, in comparison to National Library of Medicine (NLM) indexing.
    Materials and methods: For 2016 (manual NLM) and 2025 (automated NLM) published articles, we conducted a limited evaluation of the TM model, with articles that received the most confident predictions, with which NLM assignments disagreed. Ground truth was established through independent dual annotation for 4 prominent study designs: cohort, case-control, cross-sectional, and case report.
    Results: For 3 designs (case-control, case report, and cross-sectional), the articles having the top 100 predictive TM scores (when NLM failed to assign that design) were judged to exhibit that design in the great majority (86%-100%) of cases. Conversely, the articles having the lowest 100 predictive TM scores (when NLM did assign the study design) exhibited the design only in relatively few (0%-21%) of cases. The most confident predictions of the TM model were highly accurate and not redundant with automated NLM indexing; the exception was cohort studies articles, in which both TM and NLM labels showed high error rates of both omission and commission.
    Discussion: TM may have value for identifying articles exhibiting study designs, which is important for clinical decision-making and evidence syntheses.
    Conclusion: NLM's cohort studies indexing is unreliable as a gold standard for building automated systems, warranting efforts to create a new manually annotated corpus.
    Keywords:  National Library of Medicine (US); bibliographic; databases; evidence-based medicine; indexing; study characteristics; study design
    DOI:  https://doi.org/10.1093/jamiaopen/ooag197
  5. Bioinformatics. 2026 Oct 22. pii: btag795. [Epub ahead of print]
    BioHarness: Substrate-Aware Evidence Assembly for Biomedical Question Answering across Literature, Knowledge Bases, and Biological Atlases.
    Meng Xiao, Chuan Qin, Jinmiao Chen, Yihang Cheng, Yuanchun Zhou, Hengshu Zhu.
       MOTIVATION: Biomedical question answering often requires evidence beyond topically retrieved literature, including gene alias resolution, database identifier normalization, and atlas-derived biological measurements. However, existing retrieval-augmented generation (RAG) systems typically follow a fixed workflow and lack an explicit mechanism for deciding when retrieved text is sufficient, when biomedical entity mapping is needed, or when executable evidence assembly over structured measurements should be invoked. This motivates a substrate-aware large language model (LLM) harness that selectively assembles sufficient evidence by combining literature retrieval, biomedical entity mapping, and access to atlas-derived measurements.
    RESULTS: We introduce BioHarness, an LLM harness for staged biomedical evidence assembly across literature retrieval, biomedical entity-mapping resources, and atlas-derived structured measurements. Across 22,123 biomedical QA items spanning seven answer formats, BioHarness improves the pooled score from 62.2 to 67.2 over the strongest baseline. Ablations, case studies, and backbone-scaling analyses show that these gains arise from repairing evidence-substrate mismatches through reranking, entity grounding, and structured measurement access, rather than from indiscriminately invoking more reasoning steps, retrieving additional literature, or relying on a particular answer-model scale. Comparisons with the frontier models and harness show that BioHarness, despite using an open-weight backbone with only 3B active parameters, approaches frontier-level performance on selected literature QA tasks and outperforms frontier harnesses without web access on biomedical identifier and locus lookup.
    Keywords:  biological atlases; biomedical knowledge bases; biomedical question answering; evidence assembly; large language models; retrieval-augmented generation
    DOI:  https://doi.org/10.1093/bioinformatics/btag795
  6. Methods Mol Biol. 2027 ;3079 29-45
    RRI-Senti: Transformer-Based Sentiment Analysis of Biomedical Literature for RNA-RNA Interaction Disease Insights.
    Punitha Chokalingam, Swetha Varadarajan.
      The rapid growth of biomedical publications has created a critical need for tools that can extract meaningful insights from RNA-RNA interaction (RRI) literature. This study presents RRI-Senti, a literature-driven framework that applies sentiment analysis to scientific publications to infer potential disease relevance and emerging research trends in RRIs. By leveraging Transformer-based models-including RIScoper, BioBERT, SciBERT, and PubMedBERT-RRI-Senti encodes contextual semantic representations of RNA entities and their interactions, enabling automatic extraction of sentiment signals related to experimental findings, therapeutic strategies, and research consensus. We provide a systematic comparison of these models, discuss their practical applications in literature curation and hypothesis generation, and highlight how sentiment-aware NLP can enhance understanding of RNA biology. This framework represents one of the first efforts to integrate sentiment analysis specifically with RNA-RNA interaction literature, offering a novel approach to guide future research priorities and knowledge synthesis in the field.
    Keywords:  BioBERT; Biomedical literature mining; Biomedical text mining; Domain adaptation; Healthcare informatics; NLP; PubMedBERT; RIScoper; RRI analysis; SciBERT; Sentiment analysis; Transformer models
    DOI:  https://doi.org/10.1007/978-1-0716-5567-2_3
  7. Bioinformatics. 2026 Oct 24. pii: btag794. [Epub ahead of print]
    Large-scale semantic indexing of Spanish biomedical literature using contrastive transfer learning.
    Ronghui You, Tianyang Huang, Ziye Wang, Yuxuan Liu, Hong Zhou, Shanfeng Zhu.
       MOTIVATION: The exponential growth of biomedical literature has made automatic indexing essential for advancing biomedical research. While automatic indexing has made strides in English biomedical literature, there has been limited research on non-English biomedical texts due to insufficient high-quality training data.
    RESULTS: We propose BERTDeCS, a novel deep learning framework for automatically indexing Spanish biomedical literature using contrastive transfer learning. BERTDeCS utilizes a multilingual BERT (M-BERT) to generate multi-language representations and adapts M-BERT for Spanish biomedical literature domain through contrastive learning. Additionally, BERTDeCS enhances its semantic indexing capabilities on Spanish biomedical literature by leveraging enriched English annotated literature in MEDLINE through transfer learning. Experimental results on Spanish datasets demonstrate that BERTDeCS outperforms state-of-the-art indexing methods, achieving top performance in the MESINESP and MESINESP2 Tasks on medical semantic indexing in Spanish within the BioASQ challenge. Additional experiments on Portuguese provide preliminary evidence that BERTDeCS can transfer to a closely related language sharing substantial lexical and biomedical terminology with Spanish.
    AVAILABILITY: The source code is available at https://github.com/yourh/BERTDeCS and at https://doi.org/10.5281/zenodo.23212468.
    DOI:  https://doi.org/10.1093/bioinformatics/btag794
  8. Nucleic Acids Res. 2026 Oct 27. pii: gkag1057. [Epub ahead of print]
    SureChEMBL in 2026: an open database of chemical and biomedical annotations from the patent literature.
    Nicolas Bosc, Tevfik Kizilören, Eloy Félix, Muhammad Arsalan, Carlos A Moreno-Vélez, James Webster, Ricardo Arcila, Melissa F Adasme, Idil Ismail, Michał Nowotka, Yury Zavarin, Maria P Magariños, Maria J Falaguera, Anna Gaulton, Anne Hersey, Andrew R Leach, Barbara Zdrazil, Noel M O'Boyle.
      The patent literature is a major source of scientific and technical information for drug discovery, often disclosing chemical structures, biological targets, therapeutic indications, and structure-activity relationships before they appear in the scientific literature. However, patent documents are difficult to analyse at scale because of their volume, legal and technical language, heterogeneous formats, and frequent use of nonmachine-readable representations for chemical structures. Since 2015, SureChEMBL has provided open access to chemical information automatically extracted from patent documents. Here, we present a major redesign of the SureChEMBL resource. The updated platform is built on a modern architecture and includes redesigned extraction, chemistry registration, search and data-delivery workflows, together with large-scale reprocessing of the historical corpus through a unified standardization pipeline. SureChEMBL also introduces automatically generated biomedical annotations for genes, proteins, diseases, and mechanisms, linking patent chemistry to biological context. The resource currently contains 122 million patents, 44 million chemically annotated documents, 31 million chemical structures, and 1 million biomedical entity annotations, accessible through https://www.surechembl.org/, an API and the downloadable Bulk Data.
    DOI:  https://doi.org/10.1093/nar/gkag1057
  9. Trials. 2026 Oct 30. pii: 639. [Epub ahead of print]27(1):
    Efficacy of a training program to support the application of the Guideline Evidence-based Health Information: a randomized controlled trial and process evaluation.
    Julia Lühnen, Birte Berger-Höger, Burkhard Haastert, Jana Hinneburg, Jürgen Kasper, Anke Steckelberg.
       BACKGROUND: The Guideline Evidence-based Health Information addresses health information providers and aims to improve the quality of health information. This study aimed to evaluate the efficacy of a training program for health information providers to support the application of the guideline. We expected the intervention to improve the quality of health information compared with providing the guideline alone.
    METHODS: In this superiority randomized controlled trial, providers responsible for producing and publishing health information were allocated 1:1 to the intervention (guideline and training program) and control (publicly available guideline) groups using computer-generated block randomization. The e-learning program (31 h, offered over 6-7 weeks) comprised modules on evidence-based medicine and the application of the guideline. Each provider committed to producing a single piece of health information informing a health-related decision on the topic of their choice. The primary outcome was the quality of health information, operationalized as adherence to the guideline recommendations, as measured using the Mapping Health Information Quality (MAPPinfo) checklist. Means were compared between groups using a t-test. The assessment and analyses were blinded to the group allocation. An accompanying process evaluation was conducted.
    RESULTS: We included 18 health information providers (nine in each group), with a total of 54 individual participants. Eight health information items were assessed in each group. The primary analysis estimated the mean MAPPinfo checklist values at 30.2% (95% CI, 24.6 to 35.9) for the intervention and at 22.7% (95% CI, 17.4 to 27.9) for the control group. The difference between groups was estimated at 7.6% (95% CI, - 0.2 to 15.3; p = 0.055). Individual and structural implementation barriers of the guideline recommendations were identified.
    CONCLUSIONS: We did not achieve the targeted sample size of 26 providers. A lack of resources was one of the main barriers to recruitment. Although our study did not provide conclusive evidence regarding the primary outcome, the participants rated the training program as relevant. Therefore, the training program will be made available to health information providers upon request.
    TRIAL REGISTRATION: ISRCTN registry, ID: ISRCTN96941060. Registered on March 7, 2019.
    STUDY PROTOCOL: https://doi.org/10.1186/s13063-020-04287-1 (Trials 2020).
    Keywords:   Guideline Evidence-based Health Information ; Evidence-based medicine; Guideline implementation; Health information; Training program
    DOI:  https://doi.org/10.1186/s13063-026-10057-2
  10. Musculoskeletal Care. 2026 Dec;pii: e70267. [Epub ahead of print]24(4):
    Accuracy, Quality and Readability of Knee Osteoarthritis Information on Social Media (Instagram e TikTok) and Search Platforms (Google e YouTube): A Cross-Sectional Study.
    Rayane Quintão Castro, Giovanna Ferreira de Oliveira, Marcella Nogueira Marques, Victória Beatriz Silva, Diogo Simões Fonseca, Carla Malaguti.
       BACKGROUND: Knee osteoarthritis (KOA) is a chronic condition with a high global prevalence, often resulting in functional decline. Faced with this, many people turn to the internet to find information about the disease and available treatments. Besides the amount of content available online, it does not ensure its accuracy, quality and readability. This study aims to evaluate the accuracy, quality and readability of KOA-related information available on Instagram, TikTok, Google and YouTube in Brazilian Portuguese.
    METHODS: Google and YouTube searches were performed using 'Tratamento de osteoartrite de joelho', 'Tratamento de artrose no joelho' and 'Tratamento de artrite no joelho'. For each term, the first 20 results were selected. Searches on Instagram and TikTok were conducted using the hashtags: #artrosenojoelho, #artrosedojoelho, and #artrosedejoelho, selecting the first 100 posts for each term. All the research was done on June 19, 2024. Content accuracy was assessed against a reference resource on knee osteoarthritis, information quality was assessed using DISCERN, and the readability of Google content was evaluated using the Flesch-Kincaid Grade Level scale.
    RESULTS: We found that the information about medical management was the most classified as inaccurate. The overall DISCERN score indicated low quality (2.3 ± 0.7) and 78% of Google texts required at least 14 years of schooling to read.
    CONCLUSION: As digital platforms have become a widely used source of health information, our findings raise concerns about the potential for misinformation to delay effective treatment and perpetuate low-value care.
    Keywords:  accuracy; knee osteoarthritis; quality; readability
    DOI:  https://doi.org/10.1002/msc.70267
  11. Cureus. 2026 Sep;18(9): e116893
    Accuracy, Relevance, and Redundancy of ChatGPT-5.3 References in Open Fracture Management: A Methodological Analysis.
    Gündüz Ercan Kutluay, Yusuf Kondil.
       OBJECTIVE: This study aimed to evaluate the bibliographic accuracy of the references that ChatGPT-5.3 (OpenAI, San Francisco, CA) presented as PubMed-indexed sources for open fracture management and determine whether the verifiable references were relevant to the topics they were meant to support.
    METHODOLOGY: In a single session in March 2026, ChatGPT-5.3 (Plus subscription), with the web search function enabled, was asked to systematically describe the management of open fractures. The model was then prompted to provide three PubMed-indexed references for each topic it had generated. ChatGPT-5.3 structured open fracture management into 15 topic categories and produced a total of 45 references. Two independent reviewers assessed each reference for bibliographic accuracy and content relevance. Bibliographic accuracy was scored from 0 to 2, and content relevance from 0 to 1. Final scores were determined by consensus. Inter-rater agreement was assessed using Cohen's kappa coefficient.
    RESULTS: Of the 45 references, 11 (24.4%) were fully bibliographically accurate. Nineteen references (42.2%) existed but contained errors in the PMID, year, journal, title, or other bibliographic details. Fifteen references (33.3%) could not be verified as valid PubMed-indexed articles. The overall bibliographic accuracy rate was 11 (45.6%), the content relevance rate was 30 (66.7%), and the overall performance score was 52.6%. All bibliographically verifiable references were content-relevant to their assigned open fracture management topics, with 30 (66.7%) references being content-relevant. The 45 references corresponded to only 36 unique sources; a single review article was reproduced under 8 (53.3%) of the 15 topic categories and accounted for 8 (72.7%) of the 11 fully accurate references. In a sensitivity analysis restricted to unique sources, the bibliographic accuracy rate decreased to 13 (37.5%). Inter-rater agreement was high, with Cohen's kappa values of 0.966 for bibliographic accuracy and 0.951 for content relevance.
    CONCLUSIONS: Although ChatGPT-5.3 generated a clinically reasonable and systematic framework for open fracture management, the references provided to support this response showed substantial bibliographic reliability problems. Because all verifiable references were content-relevant, the model's principal limitation appeared to lie not in the selection of irrelevant sources, but in the presentation of seemingly appropriate sources with inaccurate or unverifiable bibliographic information. References generated by ChatGPT should not be used for academic or clinical purposes without independent verification.
    Keywords:  artificial intelligence; bibliographic accuracy; chatgpt; open fracture; orthopedic trauma; reference accuracy
    DOI:  https://doi.org/10.7759/cureus.116893
  12. BMC Med Inform Decis Mak. 2026 Jul 30. pii: 433. [Epub ahead of print]26(1):
    Evaluating the reliability, quality, and readability of AI-generated patient education on hallux valgus: a comparative study of large language models.
    Ali Can Koluman, Ebru Aloglu Ciftci, Mehmet Utku Ciftci, Başar Burak Cakmur, Nezih Ziroglu.
       BACKGROUND: Patients with hallux valgus increasingly seek health information through consumer-facing artificial intelligence (AI)-driven patient education tools, particularly large language model-based conversational agents. Although these tools offer rapid and accessible responses, concerns remain regarding the reliability, usefulness, overall quality, and readability of AI-generated patient education materials. Evidence specifically evaluating AI-generated patient education for hallux valgus, a condition strongly influenced by patient expectations and treatment preferences, remains limited.
    METHODS: This cross-sectional comparative study evaluated the performance of three large language models-ChatGPT-4o, Gemini-2.5-Flash, and DeepSeek-V3-in responding to 20 patient-centered questions related to hallux valgus. Questions were developed using AI-assisted question generation and publicly available Google Trends search patterns and categorized into four clinical domains. AI-generated responses were anonymized and independently assessed by three orthopaedic surgeons for reliability, usefulness, and overall quality using 7-point Likert-based reliability and usefulness scales and the Global Quality Scale (GQS). Readability was analyzed using six standardized indices. Inter-rater agreement and between-model comparisons were statistically evaluated.
    RESULTS: Gemini-2.5-Flash demonstrated modestly higher overall reliability, particularly in questions related to etiology and clinical presentation. DeepSeek-V3 achieved higher usefulness scores in the long-term outcomes and quality-of-life domain and produced significantly more readable content, as reflected by higher Flesch Reading Ease scores and lower grade-level indices. In contrast, Gemini-2.5-Flash generated linguistically more complex responses requiring higher educational levels for comprehension. Overall usefulness and global quality scores did not differ significantly among models. Qualitative review also identified occasional examples of oversimplified or potentially misleading information. Despite these differences, several readability metrics exceeded recommended patient health-literacy thresholds.
    CONCLUSIONS: Contemporary AI-based conversational agents can provide patient-oriented information regarding hallux valgus with variable reliability and readability characteristics, although statistically significant differences were observed across models. A trade-off between factual accuracy and linguistic accessibility was observed. AI tools should therefore be regarded as adjuncts to, rather than replacements for, clinician-led patient education. Awareness of AI limitations and appropriate clinical guidance remain essential to ensure safe, accurate, and patient-centered information delivery.
    CLINICAL TRIAL NUMBER: Not applicable.
    Keywords:  Artificial intelligence; Hallux valgus; Health literacy; Large language models; Patient education; Readability
    DOI:  https://doi.org/10.1186/s12911-026-03722-8
  13. JMIR Dermatol. 2026 Oct 30. 9 e99780
    Quality and Readability of ChatGPT and Gemini Responses to Patient Questions About Systemic Isotretinoin: Comparative Evaluation Study.
    Elif Bal Avcı, Buğra Burç Dağtaş, Dilara İlhan Erdil, Ozan Erdem, Andaç Salman, Selçuk Korkmaz, Sezgi Sarıkaya Solak.
       Background: The use of AI in health care is rapidly expanding due to its accessibility, with growing reliance among both physicians and patients.
    Objective: This study evaluated ChatGPT (OpenAI) and Gemini (Google) responses to patient questions on systemic isotretinoin.
    Methods: A total of 40 commonly anticipated patient questions were developed based on online sources and patient education materials and categorized into 5 groups. Responses were generated using the January 2025 free versions of ChatGPT (GPT-4o) and Gemini 2.0 Flash. A total of 5 experienced dermatologists evaluated the responses in a blinded manner using a 5-point Likert scale assessing accuracy, clarity, completeness, usefulness, and overall quality score (Global Quality Score [GQS]). Readability was measured using Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease, and Simple Measure of Gobbledygook (SMOG) index scores. Mixed-effects modeling was performed to adjust for reviewer and question variability, and intraclass correlation coefficients (ICCs) were calculated to assess interrater reliability.
    Results: ChatGPT achieved significantly higher ratings in accuracy, completeness, usefulness, and GQS, while clarity showed a nonsignificant trend. When questions were analyzed by subtype, no statistically significant differences were observed between models across evaluation parameters. Mixed-effects modeling confirmed higher odds of superior ratings for ChatGPT across domains (odds ratios [ORs] 1.82-5.88; all P<.001). However, interrater reliability was lower for ChatGPT (global ICC 0.401) compared with Gemini (global ICC 0.677). Readability analyses demonstrated that ChatGPT required a higher reading level (FKGL 12.6 vs 11.2; SMOG 11.3 vs 10.0).
    Conclusions: ChatGPT outperformed Gemini in terms of data accuracy and quality, while Gemini offered superior readability and consistency. Both models exceeded recommended readability thresholds, highlighting a gap between informational quality and linguistic accessibility in AI-generated health communication.
    Keywords:  AI; artificial intelligence; large language models; patient education; readability; systemic isotretinoin
    DOI:  https://doi.org/10.2196/99780
  14. J Craniofac Surg. 2026 Oct 23.
    Quality, Transparency, and Readability of Artificial Intelligence Chatbots as Sources of Plagiocephaly Information: A Cross-Sectional Comparative Study.
    Rui Li, Dan Zhang, Gang Qian, Yingying Ma, Guangbin Xu, Jiawen Wang, Yanghua Peng, Guo Wei.
      Parents increasingly use artificial intelligence chatbots for information about plagiocephaly, but the quality and readability of their responses remain uncertain. This cross-sectional study compared 4 mainstream artificial intelligence chatbots using 37 high-frequency English queries identified through Google Trends for "plagiocephaly" worldwide over the previous 5 years. On August 18, 2026, the queries were submitted to ChatGPT 5.5, Gemini 3.5 Flash, Perplexity, and Copilot, generating 148 responses. Information quality was assessed using DISCERN, Ensuring Quality Information for Patients, the JAMA Benchmark Criteria, and the Global Quality Scale; readability was assessed with 6 established indices. Between-model differences were tested using the Friedman test, with Kendall W effect sizes and Holm-adjusted paired Wilcoxon signed-rank tests. Quality scores differed significantly among models. Perplexity had the highest median DISCERN score [50.00 (47.00, 53.00)] and Gemini the lowest [38.00 (35.00, 40.00)]. Copilot had the highest median Ensuring Quality Information for Patients score [85.00 (75.00, 90.00)] and Gemini the lowest [60.00 (55.00, 60.00)]. Median JAMA scores were 1.00 for ChatGPT, Perplexity, and Copilot and 0.00 for Gemini; the median Global Quality Scale score was 4.00 for all models. None met recommended readability thresholds: Flesch-Kincaid Grade Level ranged from 11.66 to 13.14, Simple Measure of Gobbledygook from 12.77 to 15.10, and Flesch Reading Ease from 34.60 to 45.71. Artificial intelligence chatbots provided coherent, relatively complete plagiocephaly information, but quality varied, source transparency was limited, and readability was poor. Their responses should supplement professional assessment, with patient-facing content requiring professional review, source traceability, and lower reading levels.
    Keywords:  Artificial intelligence; health information quality; large language models; patient education; plagiocephaly
    DOI:  https://doi.org/10.1097/SCS.0000000000013552
  15. Diagnostics (Basel). 2026 Oct 21. pii: 3430. [Epub ahead of print]16(20):
    Quality, Readability, Understandability, and Actionability of AI Chatbots' Responses to Dental Patient Questions: A Comparative Evaluation.
    Nebiha Gozde Ispir, Gokalp Aslan, Gulsun Akay.
      Background/Objectives: Artificial intelligence (AI) chatbots are increasingly used to obtain oral health information, yet prior dental studies have already evaluated overlapping domains such as accuracy, quality, readability, understandability, and actionability. This study applied and extended a multidimensional evaluation approach to compare AI-generated answers to dental patient questions, with emphasis on Turkish-language readability, standard PEMAT-P applicability, effect-size and uncertainty reporting, and inter-rater reliability. Methods: Thirty-three AI-assisted, expert-curated questions were submitted to ChatGPT 5.2, Gemini, DeepSeek V3, and Grok 3, yielding 132 responses. Three dental evaluators independently assessed clinical accuracy, Global Quality Score (GQS), understandability (PEMAT-U), and actionability (PEMAT-A). PEMAT-P scores were calculated using the standard item structure and item-specific applicability rules, with items coded as not applicable (N/A) excluded from the denominator. Readability was calculated using a deterministic, rule-based implementation of the Ateşman formula. Model comparisons used the Friedman test, Bonferroni-adjusted Wilcoxon signed-rank tests, effect sizes, bootstrap confidence intervals, and two-way random-effects intraclass correlation coefficients. An exploratory Pareto analysis assessed multidimensional dominance without imposing arbitrary weights. Results: Significant model differences were observed for all five dimensions (all p ≤ 0.002). Gemini had the highest mean GQS, clinical accuracy, PEMAT-U, and PEMAT-A scores, but its Ateşman readability score was significantly lower than those of ChatGPT, DeepSeek, and Grok. No significant readability differences were observed among the latter three models. Average-measure reliability was poor for GQS and clinical accuracy and moderate for PEMAT-U and PEMAT-A. No model dominated another across all five dimensions. Conclusions: The chatbots demonstrated model-specific strengths rather than uniform superiority. Their performance should be interpreted multidimensionally, together with effect magnitude, uncertainty, and inter-rater reliability, rather than through a single overall ranking.
    Keywords:  PEMAT; Pareto analysis; artificial intelligence; dental patient education; effect size; large language model; multidimensional evaluation; readability
    DOI:  https://doi.org/10.3390/diagnostics16203430
  16. Health Informatics J. 2026 Oct-Dec;32(4):32(4): 14604582261503854
    Reliability and readability assessment of appendicitis information from AI language models (ChatGPT, gemini and perplexity) in English and Spanish.
    Mabel Lucero Olarte Jurado, Natalia Pimiento Blanco, María Jose Prieto Otero, Miguel Enrique Ochoa Vera, Jaime Fernando Barajas, Cristian Orlando Porras Bueno.
      Background and aimsAppendicitis is one of the most frequent conditions in emergency departments. Artificial intelligence (AI)-based language models represent a novel source of medical information; however, their reliability and readability across languages remain uncertain. This study aimed to evaluate the reliability and readability of AI-generated appendicitis information.MethodsSeven standardised questions were formulated in Spanish and English. Two independent reviewers assessed response reliability using a validated tool. Spanish readability was measured using the Szigriszt-Pazos formula, while English readability was assessed via the Flesch Reading Ease (FRE) and Flesch-Kincaid scores. Results were compared across tools and languages.ResultsFor Spanish reliability, Perplexity® produced the highest proportion of complete responses (71.43%), followed by ChatGPT® (57.14%) and Gemini® (28.57%). In English, Perplexity® and Gemini® both achieved 71.43%, followed by ChatGPT® (57.14%). Readability analysis via FRE formula in English revealed significant differences between the models (p < 0.05). Perplexity® showed the best reliability but yielded text that was difficult to read in Spanish and very difficult in English.ConclusionPerplexity® emerged as the most reliable model in both languages, although readability remains a concern. There is a clear need for improvements to optimize the accuracy and accessibility of AI-generated medical information.
    Keywords:  Appendicitis; Artificial Intelligence; Large Language Models; Readability
    DOI:  https://doi.org/10.1177/14604582261503854
  17. Acta Neuropsychiatr. 2026 Oct 23. 38 e58
    Evaluation of ChatGPT responses to common questions about rTMS: can ChatGPT support patient education about rTMS?
    Büşra Kalem, Hatice Gülhan Gülel, Theokritos Veskoukis, Eva Meisenzahl, Milenko Kujovic, Christian Theisen.
       OBJECTIVE: In recent years, artificial intelligence (AI) and its applications have become a compelling topic in psychiatry. This study aimed to investigate whether the AI chatbot ChatGPT is useful for patient education by evaluating the quality, reliability, and readability of its responses to patient questions about repetitive transcranial magnetic stimulation (rTMS).
    METHODS: Twenty-five frequently asked questions about rTMS were separately queried to ChatGPT-4 with no follow-up questions. Responses were reviewed by three psychiatrists by using the Global Quality Scale (GQS), DISCERN tool, and Journal of the American Medical Association (JAMA) Benchmark Criteria. Readability was evaluated by the Flesch-Kincaid Grade Level and the Gunning Fog, Coleman-Liau, and Simple Measure of Gobbledygook Indexes.
    RESULTS: Overall, the quality of ChatGPT responses was moderate to good, with a mean GQS score of 3.69. The mean DISCERN score was 35.33, which is considered poor, and the JAMA Benchmark Criteria score was 0 for all questions. The mean readability score was high (mean grade level, 10.98).
    CONCLUSION: Our results show that ChatGPT responses to patient questions about rTMS are of acceptable quality and usefulness. However, the reliability and accuracy of the responses are limited, and a high reading level is required to understand them. Although ChatGPT can support patient education about rTMS, it should be used with caution and under guidance from healthcare professionals.
    Keywords:  ChatGPT; artificial intelligence; noninvasive brain stimulation; patient education; transcranial magnetic stimulation
    DOI:  https://doi.org/10.1017/neu.2026.10100
  18. Am Surg. 2026 Oct 26. 31348261502277
    Clinical Safety Thresholds and Decision Risks of Search-Augmented Large Language Models in Acute Pancreatitis: A Guideline-Anchored Evaluation for Surgical Practice.
    Burak Altunpak.
      BackgroundPatients and clinicians increasingly consult search-augmented Large Language Models (LLMs) for point-of-care decision support. However, their safety thresholds against newly revised guidelines remain unverified. This study established a safety baseline evaluating the clinical accuracy, completeness, quality, and readability of ChatGPT-5.3 and Gemini 3.0 Flash anchored to the 2025 IAP/APA acute pancreatitis guidelines.MethodsTwenty-seven consensus-derived clinical scenarios spanning diagnosis, management, intervention, and discharge were queried using zero-shot prompts. Three blinded general surgeons evaluated outputs using 5-point Likert scales for accuracy, completeness, and Global Quality Score (GQS), alongside binary error metrics. Readability was quantified via FRES, FKGL, and SMOG indices. Inter-rater agreement was assessed using Intraclass Correlation Coefficients (ICC).ResultsGemini demonstrated significantly higher completeness than ChatGPT (median [IQR]: 4.33 [4.33-4.67] vs 4.33 [4.00-4.33]; corrected P = .042); Global Quality Scores did not differ significantly (corrected P = .231). Clinical accuracy was comparable (P = .217). Response-level hallucination rates were similar (ChatGPT: 25.9% vs Gemini: 22.2%; P = 1.000). One fatal error (advocating premature necrosectomy) occurred in the Gemini cohort (3.7%). High-score concentration induced a ceiling effect, attenuating ICC values. Both models operated at a 12th-grade reading level. ChatGPT's quality correlated positively with word count (ρ = 0.419, P < .05), whereas Gemini maintained volume-independent density.ConclusionBoth LLMs display strong baseline fidelity to the 2025 IAP/APA guidelines. However, persistent critical errors and high academic reading levels preclude autonomous deployment. Establishing this safety floor is an indispensable prerequisite for human-in-the-loop usability trials, positioning LLMs strictly as supervised clinical co-pilots.
    Keywords:  ChatGPT; Gemini; acute pancreatitis; artificial intelligence; clinical decision support; large language models
    DOI:  https://doi.org/10.1177/00031348261502277
  19. Front Med (Lausanne). 2026 ;13 1973075
    Comparative evaluation of large language models for esophageal cancer patient education: quality, reliability, readability, understandability, and actionability.
    Ying Ying Hang, Rui Yuan, Man Lu, Fang Wen Long, His Quan Hsu, Lu Bai, Jun Qian.
       Background: Large language models (LLMs) are increasingly used as sources of health information. This study evaluated the quality, reliability, readability, understandability, and actionability of responses generated by three mainstream LLMs to esophageal cancer-related questions.
    Methods: Thirty esophageal cancer-related questions covering six domains were identified from Google Trends, clinical practice guidelines, and social media and submitted to three LLMs, yielding 90 responses. Responses were evaluated using the Global Quality Scale (GQS), modified DISCERN (mDISCERN), Ensuring Quality Information for Patients (EQIP), Quality Assessment of Medical Artificial Intelligence (QAMAI), Patient Education Materials Assessment Tool (PEMAT), and six readability indices. Differences among the models were assessed using repeated-measures ANOVA or the Friedman test according to data distribution.
    Results: Crucial information missing was the most common content deficiency (8.9%). Overall GQS, mDISCERN, QAMAI, and EQIP scores were 4.00 (IQR: 4.00-4.00), 4.00 (IQR: 3.50-4.00), 22.00 (IQR: 21.50-25.00), and 71.88 (IQR: 62.50-75.00), respectively. DeepSeek performed best overall, followed by ChatGPT and Doubao. None of the three models met the recommended sixth-grade reading level, and all had PEMAT-A scores below the recommended 70% threshold. Readability was generally not significantly associated with quality or reliability, except for a few weak negative correlations.
    Conclusions: LLMs demonstrated generally favorable quality and reliability when answering esophageal cancer-related questions, but readability and actionability remained limited. They may serve as supplementary tools for patients seeking esophageal cancer-related health information but should not replace professional medical consultation.
    Keywords:  esophageal cancer; health information quality; large language models; patient education; readability
    DOI:  https://doi.org/10.3389/fmed.2026.1973075
  20. J Vis Exp. 2026 Oct 23.
    Content and Quality Analysis of Chikungunya Fever-Related Online Information on Social Media: A Cross-Sectional Study.
    Jialin Lu, Yiran Gao, Min Wei, Yanping Wang, Peng Wang, Nan Meng, Huan Liu, Kexin Wang, Qunhong Wu.
      An outbreak of chikungunya fever (CHIKF) occurred in China in 2025. The rapid increase in new cases has drawn the attention of health authorities. The outbreak also sparked widespread discussion on social media platforms. Fear-inducing and uncertain information about CHIKF circulated rapidly online, which may complicate risk communication and outbreak management efforts. In this study, we assessed the content characteristics, video quality, information reliability, transparency, and engagement of CHIKF-related short videos across the two platforms. We searched TikTok China and Bilibili using the term "chikungunya fever" and included the top 100 videos from each platform on October 9, 2025 (N = 200). Video quality was assessed using the Global Quality Scale (GQS) and the Video Information and Quality Index (VIQI); information reliability was evaluated using the modified DISCERN (mDISCERN); transparency was measured using the Journal of the American Medical Association (JAMA) benchmark criteria; and engagement was quantified using platform-based interaction metrics. Statistical analyses were performed to compare video characteristics across platforms and uploader categories, and to evaluate associations between these characteristics and quality scores. Videos from TikTok China showed higher JAMA benchmark scores, whereas videos from Bilibili achieved comparatively higher mDISCERN scores. Subgroup analyses indicated no statistical differences in engagement metrics across uploader categories, whereas quality scores differed significantly. In addition, engagement metrics showed weak correlations with video quality or reliability. The quality and reliability scores for CHIKF-related short videos on the two platforms were relatively low. The results support careful consideration of whether video quality and information reliability should be more explicitly incorporated into platform governance and content distribution strategies.
    DOI:  https://doi.org/10.3791/71918
  21. J Child Orthop. 2026 Oct 28. 18632521261500230
    Four large language models in parent-oriented education for pediatric flexible flatfoot: Quality, readability, hallucination, and reproducibility.
    Erkan Servet, Beytullah Unat, Musa Alperen Bilgin, Çağrı Karabulut, Nevzat Gönder.
       Purpose: Parents increasingly consult large language model (LLM) chatbots about pediatric flexible flatfoot, a common condition that rarely requires treatment and in which the clinical task is chiefly communication. We compared four LLMs on the quality, readability, and reproducibility of parent-oriented answers.
    Methods: This was a cross-sectional, comparative content analysis of AI-generated text. No patients, clinical records, or human participants were involved; the units of analysis were parent-oriented questions and the chatbot responses they generated. Twenty parent-oriented questions were submitted in triplicate to ChatGPT, Claude, DeepSeek, and Gemini in independent, memory-disabled sessions (240 responses). Two blinded pediatric orthopedic surgeons scored responses (Mika grade, four-point Likert, and a hallucination taxonomy; PEMAT-P, DISCERN, and QUEST on pooled material). Readability used five indices. Between-model differences were tested with Friedman and Dunn-Bonferroni post-hoc tests; agreement and reproducibility used intraclass correlation coefficients (ICC).
    Results: All five readability indices differed between models (P < 0.001); none reached the recommended sixth-grade level. DeepSeek was most accessible (FRES 66.3) and ChatGPT least (FRES 17.7). On pooled-material instruments (inter-rater ICC 0.63-0.74), Claude scored highest for understandability (91.6%) and actionability (88.3%). Per-response clinical-quality measures were closely clustered, showed poor inter-rater agreement (ICC -0.01 to +0.07), and did not discriminate. Readability reproducibility across identical queries was moderate (FRES ICC 0.49-0.72).
    Conclusions: Clinical-quality scales did not reliably distinguish the four LLMs, whereas readability differed substantially; none met recommended readability standards, and output was only moderately reproducible. Model choice should be guided by readability and actionability, under clinician oversight.
    Significance of Study: For a condition managed mainly through parent education, LLM selection should rest on readability and actionability rather than presumed clinical accuracy, with output used under clinician oversight.
    Level of Evidence: V (cross-sectional comparative content analysis of AI-generated text; no human participants).
    Keywords:  artificial intelligence; flatfoot; large language models; patient education; pediatric orthopedics
    DOI:  https://doi.org/10.1177/18632521261500230
  22. Front Med (Lausanne). 2026 ;13 1971190
    Reliability and readability of five large language models in answering patient-oriented questions about rectal prolapse: a comparative study.
    Le Zhang, Lei Liang, Xin Peng, Shihong Liu, Lingli Zhong, Zhenyu Li.
       Background: Rectal prolapse (RP) is a pelvic floor disorder that can cause rectal protrusion, defecatory dysfunction, and fecal incontinence. Patients increasingly use large language models (LLMs) for health information, yet the reliability and readability of LLM responses to patient-oriented questions about RP remain unclear.
    Objective: To compare the reliability and readability of five LLM platform-model configurations in answering English-language patient-oriented questions about RP.
    Methods: In this web-based cross-sectional study, worldwide Google Trends queries from the preceding 5 years were identified using the MeSH term "rectal prolapse." Two colorectal specialists screened the queries and standardized them into 15 patient-oriented questions. The questions were submitted to ChatGPT, Gemini, Microsoft Copilot, Perplexity, and DeepSeek under standardized single-turn, zero-shot conditions, with one response collected per question from each platform-model configuration. Two independent evaluators assessed the responses using DISCERN, the Ensuring Quality Information for Patients (EQIP) instrument, the Global Quality Score (GQS), and the JAMA benchmark criteria. Using evidence-based guidelines and question-specific checklists of key clinical content, the evaluators also independently assessed clinical factual accuracy and completeness and identified potentially harmful recommendations and critical omissions. Readability was evaluated using the Automated Readability Index (ARI), Gunning Fog Index (GFI), Flesch-Kincaid Grade Level (FKGL), Coleman-Liau Index (CL), Simple Measure of Gobbledygook (SMOG), and Flesch Reading Ease Score (FRES).
    Results: DISCERN (P = 0.001), EQIP (P < 0.001), and JAMA scores (P < 0.001) differed across configurations, whereas GQS did not (P = 0.175). ChatGPT and Copilot had the highest median DISCERN scores (44.00 for both), and DeepSeek had the lowest (35.00); ChatGPT and Copilot each scored significantly higher than DeepSeek. Copilot had the highest median EQIP score (80.00), followed by Perplexity (75.00). Median JAMA scores were 1.00 for ChatGPT, Copilot, and Perplexity and 0.00 for Gemini and DeepSeek. All five models had a median GQS of 4.00. The median clinical factual accuracy score was 5.00 (IQR, 5.00-5.00), and 69 of 75 responses (92.0%) were rated fully accurate. Median completeness was 91.7% (IQR, 87.5%-100.0%). No response contained a potentially harmful recommendation; critical omissions were found in 6 of the 50 applicable responses (12.0%). All configurations had median grade-level scores >6 and median FRES <80. No model met the prespecified sixth-grade readability standard.
    Conclusion: The five LLMs generally produced clinically accurate information about RP. No potentially harmful recommendations were identified. Yet information quality and source transparency differed among models, some responses omitted critical clinical information, and readability was poor across all five. LLMs are currently better suited as professionally reviewed adjuncts to patient education than as substitutes for specialist assessment or individualized clinical decision-making.
    Keywords:  artificial intelligence; information quality; large language models; patient education; readability; rectal prolapse
    DOI:  https://doi.org/10.3389/fmed.2026.1971190
  23. Scott Med J. 2026 Oct 26. 369330261497698
    Readability, understandability and actionability of BAUS and EAU patient education materials for common non-oncological urological conditions.
    Hai Wen Ho, Chandra Shekhar Biyani.
      Background and AimsPatient education materials (PEMs) support patient understanding and engagement in care. This study evaluated the readability, understandability and actionability of non-oncological urological PEMs produced by the British Association of Urological Surgeons (BAUS) and the European Association of Urology (EAU), and explored agreement between human and AI-assisted PEMAT-P assessment.Methods and ResultsA cross-sectional descriptive analysis assessed eight matched PEMs relating to urinary tract infection, urinary incontinence, benign prostatic enlargement/lower urinary tract symptoms, and nocturnal polyuria. Readability was assessed using Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning Fog Index. Understandability and actionability were assessed using the Patient Education Materials Assessment Tool (PEMAT-P). Mean FKGL was 11.85 ± 2.77, SMOG 13.24 ± 2.39, Gunning Fog 14.09 ± 3.22, and FRE 48.5 ± 13.32. All materials exceeded the recommended reading level. Mean PEMAT-P understandability and actionability were 91.0% and 88.3%. Human-AI item-level agreement for understandability and actionability was 83.6% and 81.8%; Cohen's κ was 0.25 and 0.23, and Gwet's AC1 was 0.79 and 0.76.ConclusionThe materials exceeded recommended reading levels despite variable PEMAT-P scores. Human vs AI differences remained, requiring further evaluation of PEMs to establish the potential role of AI as an adjunct to human PEMAT-P assessment.
    Keywords:  PEMAT; artificial intelligence; health literacy; patient education materials; readability
    DOI:  https://doi.org/10.1177/00369330261497698
  24. Acta Med Philipp. 2026 ;60(16): 75-81
    Evaluating the Quality and Reliability of Cervical Spine Isometric Exercise Videos on YouTube for Neck Pain: A Cross-sectional Study.
    Niña Ricci C Reyes, Ephraim Dv Gambito, Vania L Yu, Reynaldo R Rey-Matias, Carl Froilan D Leochico.
       Background: YouTube contains simple and easily accessible home/health hacks, including self-exercises. However, the quality/reliability of health-related content cannot always be assured, potentially leading to unreliable and harmful information. With rising digital device use, musculoskeletal cervical pain has increased, and neck exercises on YouTube have not been previously evaluated.
    Objective: This study aimed to evaluate the quality/reliability of YouTube neck isometric exercise videos.
    Methods: A cross-sectional review of YouTube videos on cervical isometric exercises for neck pain was conducted on January 21, 2024. Videos demonstrating cervical isometric exercises, published within the past 10 years, and ≤20 minutes in duration were included. Non-English, duplicate, sponsored, or non-educational videos were excluded. Eligible videos were assessed using the Global Quality Scale and modified DISCERN scale.
    Results: 21/197 videos were included. Most videos had moderate quality (52.38%), uploaded by non-physician healthcare professionals (76.19%). Videos with generally poor quality (14.29%) were mostly uploaded by independent users (66.67%). Video quality varied according to the upload source.
    Conclusion: Quality and reliability of exercise videos vary based on sources. Videos produced by non-physician healthcare professionals were higher in quality/reliability than those produced by independent users. Medical professionals and institutions can be proactive in providing more quality/reliable public health-related resources.
    Keywords:  YouTube; neck isometric exercise; neck pain; quality; reliability
    DOI:  https://doi.org/10.47895/amp.v60i16.13778
  25. Cureus. 2026 Sep;18(9): e116932
    Quality and Reliability of YouTube Videos on the Periodontal Tunnel Technique and Their Coverage of Procedure-Specific Content: A Cross-Sectional Study.
    Sahin Altug, Huseyin Akilli, Zeliha Basak Cakir Erdil, Metin Calisir.
      Objective This study aimed to evaluate the quality, reliability, and informational content of YouTube videos (Alphabet Inc., Mountain View, California, United States) related to the periodontal tunnel technique and to examine technique-specific surgical content coverage. Materials and methods This cross-sectional study included 22 YouTube videos that met the inclusion criteria. The videos were independently evaluated by three periodontists using the Global Quality Scale (GQS), Journal of the American Medical Association (JAMA) benchmark criteria, DISCERN, and a study-specific 16-item Tunnel Content Score developed with reference to the literature and expert consensus. Inter-rater reliability was assessed using the intraclass correlation coefficient (ICC) and Fleiss' κ. Mean scores from the three reviewers were used in the main analyses. Associations between quality scores and video engagement metrics were assessed using Spearman correlation analysis, and differences according to uploader type were evaluated using the Kruskal-Wallis test. Results Inter-rater reliability was moderate for GQS and JAMA and excellent for DISCERN and the Tunnel Content Score. Median scores were 3.00 for GQS, 1.00 for JAMA, 38.00 for DISCERN, and 10.17 for the Tunnel Content Score. GQS was strongly correlated with DISCERN (ρ=0.737; p<0.001), while DISCERN was moderately correlated with the Tunnel Content Score (ρ=0.494; p=0.026). No significant associations were found between video engagement metrics and quality scores, and no significant differences were observed according to uploader type (all p>0.05). Tunnel preparation and papilla preservation were included in all videos, whereas postoperative care and complications were absent. Recession classification (9.1%) and indications (31.8%) were less frequently represented. Conclusion YouTube videos related to the periodontal tunnel technique showed variability in quality, reliability, and content coverage. Technical procedural steps were more prominently represented, whereas content related to clinical decision-making and postoperative patient management was more limited. Therefore, these videos may be considered supplementary materials supporting structured clinical education and scientific sources. Studies with larger samples may contribute to supporting these findings.
    Keywords:  gingival recession; plastic periodontal surgery; social media; tunnel technique; video quality; youtube videos
    DOI:  https://doi.org/10.7759/cureus.116932
  26. BMC Med Educ. 2026 Oct 23. pii: 1755. [Epub ahead of print]26(1):
    General information reliability, perceived educational quality, and transparency of YouTube videos on epileptiform discharges: an exploratory cross-sectional study.
    Elif Ünal, Elif Simin Issı.
       OBJECTIVE: Electroencephalography (EEG) is central to diagnosing and classifying epileptiform discharges, but interpretation requires structured training. This exploratory cross-sectional study evaluated general information reliability and completeness, perceived educational quality, and transparency of English-language YouTube videos on epileptiform discharges. Although these patterns are described using established electrographic criteria, interpretation may vary, particularly in borderline recordings.
    METHODS: A cross-sectional YouTube search was conducted in May 2025 using five keywords. The first 100 results per keyword were screened and 25 of 500 records met all eligibility criteria. The final sample was limited to narrated, English-language videos lasting 1 to 20 min that were returned by the selected search terms and YouTube ranking system. Two neurologists independently rated the videos using DISCERN as a general reliability indicator, the Global Quality Scale (GQS) for educational quality, and the Journal of the American Medical Association (JAMA) criteria for transparency. Exploratory analyses included group comparisons, Spearman correlations, single-measure intraclass correlation coefficients (ICCs), effect sizes, confidence intervals, and false-discovery-rate-adjusted q values.
    RESULTS: Six videos (24%) were from academic/educational channels and 19 (76%) from individual healthcare professionals. Mean scores were 52.1 for DISCERN (95% CI 46.7-57.5), 3.86 for GQS (95% CI 3.58-4.14), and 1.98 for JAMA (95% CI 1.69-2.27). Single-measure ICCs were 0.971, 0.655, and 0.816, respectively. Duration was nominally correlated with GQS (ρ = 0.431, p = 0.031) and JAMA (ρ = 0.553, p = 0.004), and DISCERN with GQS (ρ = 0.496, p = 0.012); none remained significant after false-discovery-rate correction (all q ≥ 0.052).
    CONCLUSION: Among the small subset of eligible videos, general-purpose instruments suggested moderate-to-good average perceived educational quality and information reliability, whereas transparency was limited. Because DISCERN, GQS, and JAMA are not EEG-specific and no qualitative EEG-accuracy, pedagogical, or learner-outcome assessment was performed, the findings are exploratory. These results should not be understood as estimating the quality of all YouTube material related to EEG or epileptiform discharges, nor do they establish EEG-specific accuracy or educational effectiveness. The evaluated videos should therefore be used only as critically appraised adjuncts to expert-supervised EEG training.
    Keywords:  EEG; Epileptiform discharges; Medical education; Reliability; YouTube
    DOI:  https://doi.org/10.1186/s12909-026-10648-2
  27. Front Public Health. 2026 ;14 1940232
    Quality and reliability of cancer- and tumor-related videos on YouTube: a scoping review.
    Jianjie Zhang, Lu Xu, Yu Fu, Yajuan Wang, Fangping Shi, Tao Shi.
       Background: Cancer imposes a heavy burden on society, and YouTube has become an important channel for the public to access health information; however, the quality of cancer-related videos on YouTube varies widely. Furthermore, there is currently a lack of systematic reviews on the quality of cancer-related videos on this platform.
    Objectives: To systematically review the current state of research on the evaluation of the quality and reliability of cancer- and tumor-related videos on YouTube; to analyze trends in video quality, patterns of evaluation tool usage, the impact of uploader types on quality, and the relationship between quality and popularity.
    Methods: A scoping review approach was adopted. We conducted a systematic search of PubMed, EMBASE, Web of Science, the Cochrane Library, Scopus, CINAHL, CNKI, and the VIP database, covering the period from the inception of each database to June 1, 2026. We included original studies that evaluated the quality of cancer- and tumor-related videos on YouTube. Two researchers independently screened the literature and extracted data, with an additional independent reviewer assisting in the decision-making process.
    Results: A total of 54 studies were included, collectively analyzing a cumulative total of 5,051 individual video observations across 21 cancer-related topics. The proportion of studies reporting high-quality video samples increased descriptively from 0% in 2018-2019 to 31.3% in 2023-2025. GQS (35 instances), original DISCERN (27 instances), modified DISCERN (21 instances), and JAMA (18 instances) were the most commonly used evaluation tools. Most studies comparing uploader types reported higher quality scores for videos from healthcare professionals or institutions.
    Conclusion: The overall quality of cancer- and tumor-related videos on YouTube was generally low. The proportion of studies reporting moderate- or high-quality video samples was descriptively greater in more recent periods. Most studies comparing uploader types reported higher quality scores for videos from healthcare professionals or institutions. Greater professional involvement and platform-level strategies to identify and promote trustworthy health information may improve the educational value of these videos.
    Keywords:  YouTube; cancer; health information; scoping review; video quality
    DOI:  https://doi.org/10.3389/fpubh.2026.1940232
  28. Medicine (Baltimore). 2026 Oct 23. 105(43): e51114
    Evaluating the quality and reliability of YouTube videos about sepsis.
    Dilek Ömür Arça, İsmail Erdemir, Feyza Ertürk Şafak, Cansu Kabakçı Gök, Selin Nur Kara Doğan, Mürüvvet Odacıoğlu, Merve Oltulu Kılınç, Sevgi Yılmaz Hancı, Volkan Hancı.
      Sepsis and septic shock affect millions of people worldwide annually, causing substantial morbidity and mortality. Early diagnosis and timely intervention are lifesaving, and guidelines emphasize patient and family education to raise community awareness. This study aimed to assess the quality, reliability, popularity, and engagement metrics of sepsis-related YouTube videos. In this cross-sectional analysis, the top 100 YouTube videos retrieved using the keyword "sepsis" were evaluated on October 30, 2023. Videos were classified according to their source and content characteristics. Quality, reliability, and accuracy were assessed using the Global Quality Score (GQS), Journal of the American Medical Association (JAMA) benchmark criteria, and modified Quality Criteria for Consumer Health Information on Treatment Choices questionnaire. Of the analyzed videos, 43% contained animations, and 62% were uploaded by healthcare professionals. The median view count was 16,971 (range: 123-1,914,817), with a median view ratio of 17.36 (range: 0.14-1748.69). Statistically significant differences in view ratios were found for animation, video source, GQS, and JAMA scores (P = .007, P = .012, P < .001, and P = .026). The median Video Power Index was 16.93 (range: 0-1686.16), with statistically significant differences in animation, video source, GQS, and JAMA scores (P = .010, P = .014, P < .001, and P = .026). Median scores were 2 for GQS (range: 1-5), 2 for JAMA (range: 0-4), and 2 for modified Quality Criteria for Consumer Health Information on Treatment Choices (range: 1-5). Sepsis-related YouTube videos containing animations were viewed more frequently, whereas those uploaded by healthcare professionals achieved higher quality and reliability scores. These results indicate that both animation and credible sources strongly influence viewers' engagement and educational quality.
    Keywords:  GQS; JAMA; YouTube videos; mDISCERN; sepsis
    DOI:  https://doi.org/10.1097/MD.0000000000051114
  29. Front Digit Health. 2026 ;8 1970196
    Three-dimensional evaluation and optimization of breast cancer rehabilitation science popularization videos on mainstream video platforms: a cross-sectional study in Mainland China.
    Siqi Wu, Chang Chen, Ge Chen, Fuyou Wang, Jianxiong Wang.
       Background: With the growing number of breast cancer survivors, rehabilitation has become a critical component of comprehensive cancer care. Mainstream video platforms have emerged as a primary channel for patients to access rehabilitation information, yet the quality of such content remains unevaluated. This study aimed to systematically assess the quality and dissemination characteristics of breast cancer rehabilitation-related videos on TikTok, MicroBlog, and Today's Headline.
    Method: A cross-sectional design was employed. From June to July 2026, videos related to breast cancer rehabilitation were retrieved on three platforms using an account-free search mode. 450 valid samples (150 per platform) were selected and a three-dimensional evaluation system was established: content scientificity (modified DISCERN scale), dissemination presentation (Global Quality Scale), and source credibility (JAMA scale). Overall quality was the arithmetic mean of the three dimensions. Six evaluators conducted scoring; inter-rater reliability was tested by ICC(2,k). Post hoc power analysis confirmed sufficient power (>0.99) for detecting medium-effect differences.
    Result: The overall quality score was 2.74 ± 0.85, with a pass rate (≥3.5) of only 19.8%; a sensitivity analysis using alternative cutoffs (≥3.0 and ≥4.0) yielded pass rates of 42.4% and 6.7%, respectively, confirming the robustness of the low-quality conclusion. TikTok's overall quality, content scientificity, and source credibility scores were significantly higher than MicroBlog and Today's Headlines (all P < 0.05, η 2 = 0.11-0.16), a difference partly attributable to TikTok's higher proportion of certified physicians. One-way ANOVA revealed significant overall differences among four creator types (F = 128.6, P < 0.001, η 2 = 0.46), with Tukey post hoc tests confirming certified physicians > rehabilitation nurses > health institutions > ordinary users. Subgroup analysis showed that sexual/reproductive rehabilitation videos had the lowest quality (2.45 ± 0.91). A weak negative association was observed between the engagement composite and quality (r = -0.18, 95% CI: -0.27 to -0.09, P < 0.001).
    Conclusion: The overall quality of breast cancer rehabilitation popular science videos on Chinese mainstream platforms was low. Patients should prioritize content from certified physicians and health institutions that cite guidelines and present balanced information. Platform-specific regulation and greater participation by certified oncologists and rehabilitation specialists are urgently needed.
    Level of evidence: Level II; Cross-Sectional Study.
    Keywords:  TikTok; breast neoplasms; cancer rehabilitation; health literacy; lymphedema; social media
    DOI:  https://doi.org/10.3389/fdgth.2026.1970196
  30. BMC Gastroenterol. 2026 Sep 15. pii: 710. [Epub ahead of print]26(1):
    Quality and content assessment of short videos as sources of health information about gastroscopy in China: a cross-sectional study.
    Dan Zhang, Lanyi Li, Rongna Yang, Ziyun Zhu, Xuemin Wang, Ni Zhang, Juanjuan Ma, De Duan, Zuotao Geng, Tianqin Xiong.
       BACKGROUND: Gastroscopy is pivotal in diagnosing and treating gastrointestinal disorders. The rise of short-video platforms has facilitated public access to information about gastroscopy; however, the overall quality of these videos remains uncertain. This study aimed to evaluate the quality and content of gastroscopy-related videos available on social media.
    METHODS: Searches were conducted on Douyin, Bilibili, and Kuaishou using the Chinese term for "gastroscopy." After screening, 274 videos were selected for analysis. Quantitative content analysis was performed using the modified DISCERN, GQS, and JAMA scoring systems.
    RESULTS: Medical professionals uploaded 59.9% of the videos, with Douyin having the highest proportion. Videos by medical professionals demonstrated higher reliability than those from non-medical sources. Moreover, videos on Bilibili had higher modified DISCERN and GQS scores, whereas videos on Douyin had the highest JAMA scores. Nonetheless, overall completeness and reliability were limited when evaluated against a comprehensive educational standard, as many videos omitted essential information on contraindications and postoperative precautions. Non-medical videos focused primarily on preoperative preparation and examination process, providing limited information on definition, contraindications, and postoperative precautions. In contrast, videos produced by medical professionals demonstrated consistency across the seven evaluated aspects, with a particular emphasis on the indications for gastroscopy. Correlation analysis indicated that the modified DISCERN score was negatively associated with likes, shares, collections, and comments; however, these associations should be interpreted cautiously, as engagement metrics may reflect factors beyond content quality. Conversely, the GQS score showed no significant correlation with these engagement metrics, while the JAMA score was only negatively correlated with comments.
    CONCLUSION: The completeness and reliability of information presented in gastroscopy‑related videos are generally limited when assessed as comprehensive patient‑education resources, and vary considerably across sources. Important topics, particularly contraindications and postoperative precautions, are frequently underrepresented. These findings highlight the need to improve the completeness and reliability of gastroscopy-related information on short-video platforms. Future research should explore strategies to optimize the quality and delivery of online medical content.
    Keywords:  Gastroscopy; Gastroscopy-related videos; Patient education; Quality; Reliability; Social media
    DOI:  https://doi.org/10.1186/s12876-026-05342-y
  31. Front Public Health. 2026 ;14 1915092
    Quality, reliability, and stigmatizing content of sexual and gender minority health videos on Chinese short-form video platforms.
    Haoyang Hu, Zekai Yu, Fei Yang.
       Background: Sexual and gender minority (SGM) populations increasingly rely on digital platforms for health information, yet the quality, reliability, and stigmatizing content of SGM health videos remain unclear. This study evaluated SGM health-related short-form videos on the Chinese platforms Douyin and Bilibili.
    Methods: We conducted a cross-sectional content analysis of prominently ranked videos retrieved using 15 research-team-developed search terms during the prespecified data-collection period (January 1-May 1, 2026). For each search term, the first 10 results returned under each platform's default ranking were recorded, yielding 150 candidate records per platform before deduplication. Eligible videos were assessed for uploader type, content theme, engagement metrics, Global Quality Score (GQS), modified DISCERN reliability, medical accuracy, misinformation, and an exploratory stigma-to-affirmation communication score. Non-parametric tests, Cramér's V with 95% confidence intervals, and unadjusted Spearman correlations were used.
    Results: Of 300 candidate records, 194 videos were included (118 Douyin, 76 Bilibili). Individual users and healthcare professionals were the most common uploaders. HIV/STI prevention and mental health were the leading themes, whereas healthcare access, privacy protection, HPV vaccination, and cancer screening were underrepresented. Overall quality was moderate [median GQS = 3.0; interquartile range (IQR), 2.0-4.0], but reliability was limited (median modified DISCERN = 2.0; IQR, 1.0-3.0). High-quality and highly reliable videos accounted for 38.1% and 19.1%, respectively. Misinformation appeared in 27.8% of videos, and 17.0% contained stigmatizing, pathologizing, fear-based, or discriminatory elements. Raw cumulative engagement metrics were strongly intercorrelated but showed weak unadjusted associations with quality, reliability, accuracy, or the exploratory communication score.
    Conclusion: Among prominently ranked SGM health videos identified using the predefined search strategy, overall quality was moderate, reliability was limited, thematic coverage was uneven, and misinformation and stigmatizing communication remained present. Raw engagement metrics showed weak associations with quality-related scores. These findings describe a time-bounded algorithmic snapshot and should not be generalized to all content on either platform.
    Keywords:  LGBTQ+ health; communication framing; health misinformation; sexual and gender minority health; short-form video; social media; stigma
    DOI:  https://doi.org/10.3389/fpubh.2026.1915092
  32. Medicine (Baltimore). 2026 Oct 23. 105(43): e51290
    Quality of Douyin videos on childhood asthma: A cross-sectional content analysis.
    Xiaohui, Shenggang Ding, Haihan Zheng.
      This study aims to evaluate the information quality of childhood asthma-related videos on the Douyin (China-market counterpart of international TikTok) short-video platform. On June 1, 2025 (Beijing time, logged-out, and non-personalized account context), we searched Douyin using the Chinese search term "," selecting the top 200 videos and ultimately including 175 videos for quality evaluation. Structured content integrity assessment tools and an instrument for judging the quality of written consumer health information on treatment choices (DISCERN) were used. The correlation between video quality and characteristics (duration, likes, comments, and shares) was analyzed. Two independent investigators performed title-and-content screening with predefined operational exclusion criteria. Inter-rater reliability was calculated before consensus adjudication. Most childhood asthma videos were created by self-identified health professionals. Content integrity scores were as follows: definition (0.325 ± 0.21), symptoms (0.521 ± 0.32), risk factors (0.294 ± 0.50), evaluation (0.425 ± 0.45), management (1.24 ± 0.47), and outcomes (0.864 ± 0.38). Among evaluated videos, 160 (91.4%) had a DISCERN score ≤50. Given that DISCERN was developed for written health information and has not been formally validated for short-form video, categorical labels ("fair," "poor," and "very poor") are interpreted descriptively only. No significant differences in DISCERN dimension scores were found across 3 sorting criteria. Duration-stratified analyses within the final n = 175 dataset showed that videos with 60 to 300 seconds exhibited higher DISCERN information-reliability and overall-quality scores compared with videos ≤60 seconds; the >300 seconds stratum contained only 4 videos, and only descriptive statistics are reported for this subgroup. Top-ranked subgroup analyses were subsets sampled from the primary 175-video dataset rather than independent samples. Our analysis reveals that the quality of the sampled set of childhood-asthma-related videos retrieved from Douyin is suboptimal and lacks comprehensive information; findings cannot be generalized to all childhood-asthma-related content hosted on this platform. These findings reflect content-level deficits rather than inherent hazards of the Douyin platform.
    Keywords:  Douyin; asthma; children; patient education; video quality
    DOI:  https://doi.org/10.1097/MD.0000000000051290
  33. J Craniofac Surg. 2026 Oct 26.
    Quality and Reliability of Neck Wrinkle-Related Short Videos on TikTok and Bilibili: A Cross-Sectional Study.
    Ci Ren, Zhoufeng Jin, Ling Zhu, Jialu Yang, Jinmin Cao.
       BACKGROUND: Neck wrinkles are a common aesthetic concern, and patients increasingly seek information on short-video platforms. However, the quality and reliability of such content remain unclear.
    OBJECTIVE: To evaluate the quality and reliability of neck wrinkle-related short videos on TikTok and Bilibili.
    METHODS: On April 20-22, 2026, we searched the Chinese keyword "Jingwen" on TikTok and Bilibili. The top 150 videos per platform (default comprehensive ranking) were screened. Video characteristics and engagement metrics were recorded. Two independent reviewers assessed quality (Global Quality Score, GQS) and reliability (modified DISCERN, mDISCERN).
    RESULTS: Of 129 videos analyzed (Bilibili: 39; TikTok: 90), treatment was the dominant content category (89.7% and 95.6%, respectively), while prognostic outcomes were rarely addressed (10.3% and 0%). Bilibili videos were significantly longer (median 182.0 versus 66.5 s, P<0.001) and had higher GQS scores (mean 2.5 versus 2.0, P<0.001), whereas TikTok videos showed higher engagement. Videos by health care professionals received significantly higher GQS (P=0.012) and mDISCERN (P=0.020) scores. Engagement metrics did not correlate with quality or reliability.
    CONCLUSION: Neck wrinkle-related short videos have an imbalanced content structure, overemphasizing treatment and neglecting prognosis. Overall quality was moderate. Bilibili videos were of higher quality, and professional-uploaded content was more reliable. Platforms should encourage professional participation and comprehensive content strategies to improve patient education.
    Keywords:  Bilibili; cross-sectional study; health information quality; neck wrinkles; social media; tiktok
    DOI:  https://doi.org/10.1097/SCS.0000000000013503
  34. BMC Cardiovasc Disord. 2026 Jul 29. pii: 976. [Epub ahead of print]26(1):
    The quality and reliability of short videos about myocarditis on Bilibili and TikTok: cross-sectional study.
    Yingxuan Gong, Yuhui He, Tingjun Li.
       BACKGROUND: Myocarditis is a significant cause of cardiovascular morbidity and sudden cardiac death. Short-video platforms like TikTok and Bilibili have become primary sources of health information. However, the quality of myocarditis-related content remains unassessed, raising concerns about the risk of misinformation and public anxiety. This study aimed to systematically evaluate the quality, reliability, and content completeness of myocarditis-related short videos on TikTok and Bilibili.
    METHODS: We conducted a cross-sectional study to systematically assess 228 myocarditis-related videos (134 from TikTok and 94 from Bilibili) retrieved using the keyword "myocarditis." Video quality and reliability were evaluated using the Global Quality Scale (GQS) and the modified DISCERN (mDISCERN), with assessments made by two independent raters. We analyzed video characteristics and the coverage of key content domains.
    RESULTS: Overall, video quality was suboptimal. Platform differences were evident: Bilibili videos had longer durations (median 247.50 s [IQR: 117.25-530.75] vs. 90.50 s [IQR: 54.25-143.00], p < 0.001) and higher GQS scores (median 3.00 [IQR: 3.00-4.00] vs. 3.00 [IQR: 2.00-3.00], p < 0.01), while TikTok videos had greater user engagement. Healthcare professionals were responsible for 62.7% of the videos, which received significantly higher quality and reliability scores (GQS: median 3.00 [IQR: 3.00, 4.00]; mDISCERN: median 3.00 [IQR: 2.00, 3.00]) compared to those produced by individuals (GQS: median 3.00 [IQR: 2.00, 3.00]; mDISCERN: median 2.00 [IQR: 1.00, 3.00]) or institutions (GQS: median 3.00 [IQR: 2.00, 3.00]; mDISCERN: median 2.00 [IQR: 1.00, 3.00]). Symptom (35.96%) description was the most frequently addressed topic, while epidemiology (3.95%) and prevention (7.02%) were rarely and inadequately covered. Furthermore, video length was positively correlated with higher GQS scores.
    CONCLUSIONS: While short videos serve as a major channel for myocarditis education, significant gaps in content quality and a lack of preventive focus were identified. Engaging healthcare professionals in content creation, promoting detailed and balanced information, and implementing platform-level quality oversight are essential to improving the accuracy and public health value of these videos.
    Keywords:  Bilibili; Health information quality; Myocarditis; Short-video platforms; TikTok
    DOI:  https://doi.org/10.1186/s12872-026-06328-0
  35. Arthrosc Sports Med Rehabil. 2026 Oct 27. e70137
    TikTok Videos on the Medial Patellofemoral Ligament Have Low Educational Quality and Reliability.
    Jacopo Conteduca, Fabrizio Brindisino, Alberto Casto, Luigi Valentino, Angelo Scrimitore, Andrea Bernetti, Giuseppe Rollo.
       Purpose: To assess the quality, reliability, and engagement of TikTok videos addressing the material about medial patellofemoral ligament (MPFL).
    Methods: Between November 2 and 24, 2024, a comprehensive search of the TikTok platform was performed using both keyword-based search ("MPFL," "medial patellofemoral ligament") and hashtag-based search (#MPFL) to maximize content capture. Videos were included if they addressed the MPFL with respect to anatomy, injury mechanism, clinical evaluation, surgical management, or rehabilitation. Each video was independently assessed using the DISCERN instrument, Journal of the American Medical Association benchmark criteria, Global Quality Scale, and Patient Education and Decision Support Score. Video characteristics, including duration, number of views, likes, shares, and shares, were recorded, and correlations between engagement metrics and quality scores were analyzed.
    Results: Of 100 videos analyzed, 9% were posted by medical doctors, 3% by physiotherapists, and 88% by private users. Most videos focused on personal experience, representing 74% of the information type and 80% of the content type. The mean scores of DISCERN, Journal of the American Medical Association, Global Quality Scale, and Patient Education and Decision Support Score were 16.25 ± 4.07, 0.1 ± 0.4, 1.1 ± 0.5, and 0.6 ± 1.88, respectively. Videos uploaded by medical professionals achieved significantly higher quality scores across all metrics (P < .001). No significant differences were observed between physicians and physiotherapists (P = .173). Engagement metrics (views, likes, shares) showed moderate positive correlations with DISCERN (r = 0.31) and Patient Education and Decision Support Score (r = 0.39), whereas video duration showed no correlation with any quality index.
    Conclusions: TikTok videos related to the MPFL show low educational quality and limited reliability, with the most content produced by nonmedical users. Videos uploaded by health care professionals achieved significantly higher quality scores across all evaluated assessment tools.
    Clinical Relevance: Patients with MPFL pathology frequently seek medical information on social media platforms. Awareness of the limited educational quality and reliability of MPFL-related content on TikTok may help clinicians better counsel patients regarding online health information.
    DOI:  https://doi.org/10.1002/ars2.70137
  36. PeerJ. 2026 ;14 e21843
    The quality and reliability of short videos about Marfan syndrome on Bilibili and Douyin: a cross-sectional study.
    Tao Sun, Jing Hu.
       Background: Marfan syndrome (MS) is a rare, life-threatening connective tissue disorder. Short video platforms are now key health-information sources, yet the quality and reliability of MS-related content remain unclear.
    Methods: In a cross-sectional study, Marfan syndrome videos were retrieved from Bilibili and Douyin on 25 April 2026 using the Chinese keyword "," from new accounts under anonymous browsing. After screening, data on video characteristics, uploader type, and publicly displayed likes, comments, shares, and collections were extracted. Two reviewers independently scored all eligible videos with Global Quality Scale (GQS), modified Decision-making Information Support Criteria for Evaluating the Reliability of Non-randomised Studies (mDISCERN), Journal of the American Medical Association (JAMA) criteria, and Content Completeness Score (CCS). Descriptive statistics, non-parametric tests, Spearman correlations, and weighted Cohen's kappa were used.
    Results: 185 videos (103 from Douyin and 82 from Bilibili) were analyzed. The median video duration was significantly longer on Bilibili than on Douyin (126.00 (IQR: 59.00-377.00) vs. 64.00 (IQR: 45.50-103.50) seconds; P < 0.001). Douyin videos had been available for a significantly shorter time since publication (median: 275.00 vs. 747.50 days; P < 0.001), yet their per-day interaction counts remained significantly higher after this adjustment (all P < 0.001). Douyin videos demonstrated significantly higher absolute interaction counts, including likes (median: 2,214.00 vs. 48.00; P < 0.001), comments (240.00 vs. 0.00; P < 0.001), shares (606.00 vs. 5.50; P < 0.001), and collections (475.00 vs. 24.00; P < 0.001). In terms of quality, Douyin videos had higher GQS scores than Bilibili (median: 4.00 vs. 3.00; P < 0.001), whereas Bilibili videos showed higher mDISCERN scores (median: 3.00 vs. 2.00; P < 0.001). No significant differences were observed in JAMA scores (P = 0.245) or CCS (P = 0.274). Healthcare professionals accounted for the majority of Douyin content (78.6%), while institutional sources predominated on Bilibili (53.7%) (P < 0.001). Videos produced by professionals and institutions achieved significantly higher quality and reliability scores compared to individual uploaders. Spearman correlation analysis revealed strong correlations among engagement metrics (r = 0.84-0.94), but weak or negligible correlations between absolute interaction counts and quality scores (r = -0.16 to 0.17).
    Conclusion: Marfan syndrome-related videos on Bilibili and Douyin varied considerably in educational quality and reliability; those by healthcare professionals were generally higher quality. Within this sample, observed interaction counts showed weak or negligible associations with quality measures.
    Keywords:  Bilibili; Cross-sectional; Douyin; Marfan syndrome; Medical education; Social media
    DOI:  https://doi.org/10.7717/peerj.21843
  37. Medicine (Baltimore). 2026 Oct 23. 105(43): e51258
    Quality and reliability of sarcopenia-related short videos on Douyin: A cross-sectional content analysis.
    Ting Zhang, Jiabei Wu, Jinli Guo.
      Sarcopenia is a geriatric syndrome marked by the progressive decline in skeletal muscle mass, strength, and physical function. It typically develops insidiously and advances gradually, significantly increasing the risk of adverse outcomes such as falls, frailty, and mortality among older adults. With the rise of social media platforms like Douyin, the Chinese domestic counterpart of TikTok, for public health messaging, it is crucial to systematically evaluate the quality of content on this platform related to sarcopenia. In this cross-sectional content analysis, we collected Douyin videos related to sarcopenia using the platform's default ranking algorithm and, after applying inclusion and exclusion criteria, obtained 156 videos. For each video, we recorded user interaction metrics (likes, comments, shares, collections) and extracted associated metadata. We evaluated content quality with the Global Quality Scale (GQS), the modified DISCERN (mDISCERN), and the JAMA benchmark criteria. We conducted Spearman's correlation analyses to examine correlations among video variables, GQS, mDISCERN, and the JAMA score. The majority of the content originated from medical professionals (43.6%), with a primary focus on disease overviews (34.6%). Video quality assessments indicated low overall standards: GQS scores frequently reached 3 (44.9%), mDISCERN scores predominantly reached 2 (42.9%), and JAMA scores were concentrated at 2 (55.1%). Videos created by general users scored significantly lower than those from other sources (all P < .05). Furthermore, video duration was positively correlated with GQS (r = 0.470, P < .001) and mDISCERN scores (r = 0.363, P < .001), but not with JAMA scores (r = 0.166, P = .691). In contrast, no significant correlations emerged between quality and interaction metrics. While Douyin has the potential to raise awareness about sarcopenia, the prevalence of subpar content highlights the need for stronger oversight, credential verification, and better algorithmic prioritization to improve the quality of health information on the platform.
    Keywords:  Douyin; health information; information quality; sarcopenia; social media
    DOI:  https://doi.org/10.1097/MD.0000000000051258
  38. Cancer Rep (Hoboken). 2026 Nov;pii: e70734. [Epub ahead of print]9(11):
    Creator Type, Content Format, and Engagement Among Highly Liked Cancer-Related TikTok Search Results: A Cross-Sectional Content Analysis.
    Eunice Y Ju, Esther D Ju, Russell G Witt.
       BACKGROUND: Engagement differences by creator type among highly liked cancer-related TikTok videos remain unclear.
    METHODS: We analyzed 250 videos from five cancer searches, selecting the first 50 results per search filtered to the preceding six months and sorted by likes. Health professionals or trainees were classified as medical. The primary analysis modeled log-transformed views by creator type, adjusting for followers, video age, content type, and cancer search.
    RESULTS: Adjusted views showed no significant difference (ratio, 0.90; 95% CI, 0.44-1.85; p = 0.773). Medical creators had a higher adjusted share rate (ratio, 2.01; 95% CI, 1.24-3.24; Holm-adjusted p = 0.026). Median views per follower were lower for medical creators (2.55 vs. 16.40; ratio, 0.16; 95% CI, 0.06-0.45; Holm-adjusted p = 0.003).
    CONCLUSIONS: Despite no clear difference in absolute views, lower views per follower raise the possibility that medical creators have more limited reach beyond established audiences than nonmedical creators, while greater sharing may broaden distribution.
    Keywords:  TikTok; cancer information; cross‐sectional content analysis; health communication; social media
    DOI:  https://doi.org/10.1002/cnr2.70734
  39. Front Digit Health. 2026 ;8 1919212
    Quality, source transparency, information reliability, and domain coverage of short video-based information on feeding guidance for people with dysphagia: a cross-sectional study on 4 major Chinese social media platforms.
    Jiaying Chen, Ting Xiong, Ying Li, Yue Wang, Chen Yang, Jinghui Huang, Fanfu Fang.
       Background: Standardized feeding guidance is valuable for people with dysphagia, helping prevent aspiration, improve nutritional status, and enhance quality of life. In recent years, short-video platforms have become a key avenue for the public to access health information, providing people with dysphagia with a convenient channel to learn about feeding management. However, the quality of online health information varies considerably, making it difficult for patients to effectively distinguish reliable information from potential misconceptions, which may negatively impact clinical outcomes.
    Objective: This study aims to systematically evaluate feeding guidance content for dysphagia on mainstream Chinese short-video platforms by analyzing its quality, the transparency of its information sources, the reliability of the information, and its coverage of relevant domains.
    Method: This study retrieved and included 278 short videos on feeding guidance for dysphagia as research samples from four major Chinese mainstream short video platforms: Rednote, Bilibili, Douyin, and Kwai. Two independent rehabilitation therapists specializing in dysphagia rehabilitation evaluated the domain coverage of video content using 10 criteria derived from relevant clinical guidelines and expert consensus documents on feeding guidance for people with dysphagia. We assessed the transparency of the video's information sources, overall quality, and information reliability using the Journal of the American Medical Association (JAMA) Benchmark Criteria, the Global Quality Score (GQS), and the modified DISCERN (mDISCERN) Score. We also compared video data from different content providers and upload platforms.
    Results: Based on the identity of the video uploader, videos were classified into seven categories, comprising four categories of healthcare professionals (physiatrists, rehabilitation therapists, rehabilitation nurses, and healthcare professionals in related clinical departments) and three categories of non-healthcare professionals (non-profit organizations, individual science communicators, and people with dysphagia). The results indicated that the overall quality of feeding guidance videos for people with dysphagia was generally low (GQS median = 2, IQR = 2-3). The impact of uploader identity varied across assessment dimensions: healthcare professionals scored significantly higher than non-healthcare professionals on both the JAMA score (P < .001) and the mDISCERN score (P < .001), although the inter-group difference in the GQS score was not statistically significant; rehabilitation nurses performed best on the GQS dimension, healthcare professionals in related clinical departments achieved the highest scores on the mDISCERN dimension, and people with dysphagia scored the lowest on the JAMA dimension. Differences among platforms also exhibit dimensionality dependence: the GQS scores showed minimal variation, with only Bilibili and Kwai exhibiting a statistically significant difference (adjusted P < .05, r = .376); the JAMA scores demonstrated moderate variation, with Rednote, Douyin, and Bilibili all significantly outperforming Kwai (adjusted P < .0001, r = .387; adjusted P < .0001, r = .551; adjusted P < .05, r = .288); the mDISCERN scores showed the most pronounced differences (ε 2 = .503), with Douyin significantly surpassing Rednote, Bilibili, and Kwai (adjusted P < .0001, r = .771; adjusted P < .0001, r = .757; adjusted P < .0001, r = .595). In terms of domain coverage, food adjustment (77%) and swallowing posture adjustment (52%) had relatively high coverage rates. In contrast, high-risk domains such as medication management (4%), nutritional management (21%), and oral care (17%) exhibited severe supply gaps.
    Conclusion: Among current mainstream short-video platforms in China, the overall quality of educational content on feeding guidance for people with dysphagia still requires improvement. The reliability of this information depends on both content creators' professional backgrounds and platform ecosystems. The existing short videos currently lack sufficient support for safe decision-making and standardized self-management among people with dysphagia. There is an urgent need to implement coordinated interventions that incorporate professional expertise, platform algorithm governance, and multidimensional quality assessments to support the standardized development of a health science communication environment.
    Keywords:  dysphagia; feeding behavior; information quality; short video; social media
    DOI:  https://doi.org/10.3389/fdgth.2026.1919212
  40. J Manipulative Physiol Ther. 2026 Sep 25. pii: eJMPT-D-26-00018. [Epub ahead of print]49(1):
    Quality and engagement of #chiropractic content on TikTok: A DISCERN-based cross-sectional analysis.
    Zelin Zhou, Qian Zhang, Xiaohua He, Niu Zhang.
       Objective: The purpose of this study was to evaluate the informational quality of #chiropractic-related TikTok videos using the DISCERN instrument and to examine associations between content quality, uploader characteristics, engagement metrics, and video duration.
    Methods: The top 300 TikTok videos tagged with #chiropractic were retrieved using the Apify TikTok Scraper on April 8, 2026. Data collected included account verification status, chiropractor or health care provider identity, views, comments, shares, collects, and video duration. Informational quality was independently assessed by 2 reviewers using DISCERN.
    Results: A total of 300 videos from 190 unique accounts were included. Among these accounts, 9 (4.7%) were verified, 79 (41.6%) were identified as chiropractors, and 89 (46.8%) were classified as health care providers. The median DISCERN score was 20 (interquartile range, 18-21). Most videos were rated as very poor (96.7%) or poor (2.0%) with only 1 fair and 3 good videos; none was rated excellent. Videos uploaded by chiropractor and health care provider accounts had significantly higher DISCERN scores than nonprofessional sources (P < .001). Verified accounts generated significantly greater engagement but did not show significantly higher DISCERN scores (P = .06). DISCERN scores were not significantly correlated with views, likes, comments, shares, or collects but showed a weak positive correlation with video duration (r = 0.185, P = .001).
    Conclusion: The overall informational quality of #chiropractic-related TikTok content was low. Professional creators produced higher quality videos, whereas popularity did not reliably reflect informational quality.
    Keywords:  Chiropractic; Health Literacy; Patient Education as Topic; Social Media
    DOI:  https://doi.org/10.67400/JMPT-D-26-00018
  41. Healthcare (Basel). 2026 Oct 14. pii: 3437. [Epub ahead of print]14(20):
    Frequency of Internet Use and Cancer Information Seeking Among U.S. Adults: A Nationally Representative Analysis of the 2024 Health Information National Trends Survey (HINTS-7).
    Jonathan N Odumegwu, Emmanuel Peprah, Jackson Barth, Melody S Goodman, Kimberly A Kaphingst.
      Background: Rising cancer diagnoses and survivorship have increased demand for reliable cancer information. Online platforms offer resources that can strengthen health information and engagement; however, variable information quality, misinformation, and unequal digital access remain concerns. Little is known about how internet-use frequency, device type, and cancer history associate with cancer information seeking. Objective: To examine how internet-use frequency, device type, and personal cancer history associate with cancer information seeking. Methods: This study uses nationally representative data from 7245 respondents, weighted to represent approximately a sample of 261,682,768 US adults in the 2024 Health Information National Trends Survey 7 (HINTS 7). Weighted adjusted logistic regression was used to examine associations between cancer information seeking, internet-use frequency, and personal cancer history. Results: The odds of seeking cancer information are statistically significantly higher among those who use the internet more than once per day (adjusted odds ratio [aOR] = 2.45, 95% CI: 1.12-5.36). Individuals with a personal history of cancer also have significantly higher odds of seeking cancer information (aOR = 3.75; 95% CI: 1.23-11.5) than those with no cancer history. However, personal cancer history does not modify the relationship between internet-use frequency and seeking cancer information, suggesting that frequent daily internet exposure among US adults may independently affect the odds of seeking cancer information, regardless of personal cancer history. The proportion of participants seeking cancer information also increases significantly (p < 0.001) with educational attainment, from 24.8% among those with, at most, an 11th-grade education to 64.6% among college graduates. The odds of seeking cancer information were statistically significantly higher for college graduates (aOR = 3.06; 95% CI: 1.66-5.61), compared with those with ≤11 grades. Conclusions: Frequent internet use was associated with cancer information seeking, and there was no statistically detectable difference across personal cancer history. Public health efforts should prioritize improving digital health literacy, expanding access to credible online resources, and using culturally tailored strategies to reach populations less likely to seek cancer information.
    Keywords:  cancer survivors; health literacy; healthcare utilization; information seeking behavior; internet-use frequency; trust
    DOI:  https://doi.org/10.3390/healthcare14203437
  42. JMIR Cancer. 2026 Oct 28. 12 e101310
    Cancer Information Burden Among US Cancer Survivors Using Health Information National Trends Survey 2022 and 2024: Cross-Sectional Study.
    Jingjing Gao, Sharon V Munroe, Yue Zhang, Jason H Windett.
       Background: Digital health platforms have become increasingly important sources of health information for cancer survivors. Cancer survivors frequently rely on digital resources to obtain information related to diagnosis, treatment, survivorship, and follow-up care. In this study, cancer information burden is defined as the perceived challenges associated with seeking, understanding, and evaluating cancer-related information, including the effort required to obtain information, frustration during information seeking, difficulty understanding information, and concern regarding information quality. As the online health information environment becomes increasingly complex, some survivors may experience difficulty locating, evaluating, and understanding cancer-related information. However, limited nationally representative research has examined how cancer information burden differs across demographic and socioeconomic groups.
    Objective: This study examined cancer information burden and associated demographic and socioeconomic factors among US cancer survivors using nationally representative data from the Health Information National Trends Survey (HINTS) 2022 and 2024.
    Methods: Data were drawn from pooled cycles of the HINTS conducted in 2022 and 2024. Survey-weighted regression analyses were used to evaluate factors associated with cancer information burden among cancer survivors. Outcomes included the perceived effort required to obtain cancer information, frustration during information seeking, difficulty understanding cancer information, and concerns regarding information quality. Composite measures of cancer information burden were also examined. Models were adjusted for demographic and socioeconomic characteristics, including age, sex, race and ethnicity, educational attainment, household income, geographic region, and survey cycle.
    Results: Hispanic cancer survivors reported a significantly greater cancer information burden, including higher levels of perceived effort and frustration associated with obtaining and understanding cancer-related information, compared with non-Hispanic White survivors. Non-Hispanic Asian survivors also demonstrated elevated levels of information burden, although estimates were less precise because of smaller subgroup sample sizes. Postbaccalaureate education was associated with lower frustration during cancer information seeking. Female survivors reported lower levels of difficulty understanding cancer-related information and lower overall information burden than male survivors. Differences between the 2022 and 2024 survey cycles were not statistically significant after adjustment.
    Conclusions: Differences in cancer information burden persist among US cancer survivors. Hispanic and non-Hispanic Asian survivors, and individuals with lower educational attainment, may experience greater challenges obtaining and navigating cancer-related information. These findings support the development of accessible, culturally responsive, and health literacy-sensitive cancer communication strategies.
    Keywords:  cancer information burden; cancer survivors; digital health; generative AI; health literacy
    DOI:  https://doi.org/10.2196/101310
  43. Health Promot J Austr. 2027 Jan;pii: e70262. [Epub ahead of print]38(1):
    Public Health Information Seeking Behaviours Among Young Adults With Migrant Backgrounds in Australia: A Cross-Sectional Study.
    Muhammad Mahad, Davoud Pourmarzi.
       ISSUE ADDRESSED: Equitable access to health information is a core component of the right to health, yet young migrants in Australia may face barriers shaped by factors including language, culture, and institutional trust. This study investigated public health information-seeking behaviours among young migrants aged 18-25 years in Australia.
    METHODS: A cross-sectional study was conducted between July and September 2025 (N = 177). Data were collected on sociodemographic factors, preferred communication channels, information sufficiency, subjective norms, and perceived information-gathering capacity using an online questionnaire. Chi-square and Welch's t-tests were used to examine factors associated with active information-seeking behaviour.
    RESULTS: Participants had a mean age of 21.4 ± 1.9 years; most were female (62%) and first-generation migrants (67%), with 74% from Asian backgrounds. Over 80% were students, and nearly half held tertiary qualifications. Social media infographics, posts and short videos (46.7%) were the most preferred channels to receive public health information. 43% actively sought public health information. Active seeking was positively associated with perceived social expectations to be informed, ease of understanding information, and ability to distinguish accurate from false information (p < 0.001, p = 0.001, p = 0.022, respectively).
    CONCLUSIONS: Young migrants are more likely to actively seek public health information when social expectations and self-efficacy reinforce their roles as family and community information brokers. SO WHAT?: Strengthening eHealth literacy and co-designing culturally relevant content with health professionals on social media could enhance access and trust. Public health communication should recognise and support young migrants in their role as information brokers, equipping them with the tools and confidence to share accurate health information.
    Keywords:  information seeking behaviour; language brokering; migrants; preferred channels; public health information; social media; youth
    DOI:  https://doi.org/10.1002/hpja.70262
  44. Dtsch Arztebl Int. 2026 Sep 18. pii: arztebl-123-m1074-0118. [Epub ahead of print]123(19): m1074-m1075
    Selective Reception of the Literature.
    Dagmar Brunner.
      
    DOI:  https://doi.org/10.3238/arztebl.m2026.0118
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