Evaluating the Quality and Reliability of DILI in Chinese Videos on TikTok and BiliBili: Cross-Sectional Content Analysis Study

Evaluating the Quality and Reliability of DILI in Chinese Videos on TikTok and BiliBili: Cross-Sectional Content Analysis Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating the Quality and Reliability of DILI in Chinese Videos on TikTok and BiliBili: Cross-Sectional Content Analysis Study Ruo-gu Nie, Bing Yu, Han Wang, Sheng-ying Qin, Cong Huai, Zhi-ling Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9603197/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objective This study is a cross-sectional analysis aimed at systematically evaluating the information quality and reliability of popular science content related to drug induced liver injury (DILI) on the two major Chinese video platforms, TikTok and BiliBili, and analyzing its content characteristics. Methods On December 2025, obtained drug-induced liver injury-related video information on two platforms. Exclude content that does not meet the requirements based on the exclusion criteria, and ultimately retain the top 100 videos from each platform that meet the standards for analysis(N = 200). Two trained reviewers independently performed blinded assessments using the Global Quality Score (GQS), Journal of the American Medical Association (JAMA) benchmark criteria, and the modified DISCERN (mDISCERN) tool. Non-parametric tests were used to compare differences between groups, and Spearman correlation analysis was employed to explore the relationship between video characteristics and quality scores. Results User engagement metrics (likes, favorites, shares) for TikTok videos were significantly higher than those for BiliBili ( p <0.001). In terms of information quality, TikTok videos scored significantly higher than BiliBili on the GQS, JAMA, and mDISCERN scales ( p <0.001). There were differences in quality across content types: videos on “medication knowledge” received the highest mDISCERN reliability scores, “disease knowledge” videos scored higher in GQS practicality. Correlation analysis showed a weak positive correlation between user engagement metrics and mDISCERN scores. Among the 107 videos mentioning liver injury related drugs, chemical drugs (antibacterial, chemotherapeutic, and anti-inflammatory drugs) and traditional Chinese medicines (such as He Shou Wu) were mentioned most frequently. Conclusion The quality of DILI related content on short video platforms is related to platform algorithms, creator identity, and content type. High user engagement is not a reliable indicator of high-quality information. While current content spreads known drug risks, it tends to simplify information and focus on popular topics. Drug induced liver injury TikTok BiliBili Information quality Short videos Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Drug-induced liver injury (DILI) refers to a heterogeneous group of liver diseases caused by various prescription and non-prescription chemical drugs, biological agents, traditional herbal medicines, dietary supplements, and health products[ 3 , 10 ]. As one of the important drug-induced diseases and common causes of acute liver failure worldwide, the pathogenesis of DILI is complex, its clinical manifestations are diverse, and its diagnosis is highly challenging[ 4 ]. In China, due to the large population base, the wide variety of clinical medications, and the extensive use of traditional Chinese medicine and health supplements, the disease burden of DILI has become a public health issue that cannot be ignored in clinical practice[ 11 ]. Therefore, the prevention and early identification of DILI, as well as enhancing public awareness of DILI, have become crucial. In recent years, the deep integration of the Internet and digital media has fundamentally changed the way the public obtains and communicates health information. Social media, particularly video platforms represented by TikTok and BiliBili, have become one of the preferred channels for the public to seek medical knowledge and share illness experiences, thanks to their intuitive and vivid content presentation, rapid dissemination, and high user interactivity[ 6 , 17 ]. This shift from passive reception to active seeking has improved the efficiency of health information dissemination. However, the low entry threshold of short video platforms and their relatively lenient content review mechanisms allow any user to freely upload medical-related content, resulting in a wide variation in the quality of health information on these platforms. A large portion of the content may originate from General populations, posing risks of one-sided information, insufficient scientific basis, and even misleading commercial promotion, presenting significant challenges for controlling information quality[ 19 , 12 ]. Moreover, in specialized and complex fields such as DILI, where public awareness is relatively limited, low-quality or incorrect information may mislead patients into unnecessary fear of medication or cause them to overlook actual signs of liver injury, thereby delaying diagnosis and treatment and posing a threat to their health[ 2 ]. Currently, some studies have begun using tools such as the Global Quality Score (GQS) and the modified DISCERN (mDISCERN) to evaluate the quality of popular science videos on platforms like YouTube and TikTok that focus on specific diseases, such as liver cancer and gallstones. These studies have found that content produced by professional healthcare practitioners is generally more reliable[ 13 , 20 ]. However, the quality of short videos on DILI has not yet been studied. Therefore, we conducted a systematic evaluation of the content and quality reliability of 200 Chinese short videos related to DILI on TikTok and BiliBili, two major Chinese short video platforms. Method Ethical considerations This study is an observational study based on publicly available online information. All data were obtained from publicly accessible content on short video platforms and did not involve any personal private information or interactions with users, therefore, ethics committee approval was not required. Search Strategy and Data Collection This study is a cross-sectional study. On December 20, 2025, searches were conducted in the Chinese versions of the TikTok and BiliBili mobile applications using "药物性肝损伤" as the primary keyword. To minimize bias introduced by personalized recommendation algorithms, newly registered accounts were used for the searches. First, exclude videos that do not meet the research criteria based on the exclusion standards, and then select the top 100 for analysis according to the comprehensive ranking of the video platform (including likes, comments, shares, etc). The exclusion criteria include: non-Chinese videos, duplicate videos (same content by different creators), and videos without a clear author or title, the detailed procedure is shown in Fig. 1 . Simultaneously record the basic information of the videos, including the video title, uploader identity (doctor, pharmacist, general communicator, hospital, association, etc.), video content category, drugs causing liver injury, video duration, release date, number of likes, shares, and favorites. All extracted data are recorded in Excel (Microsoft Corporation). Video Classification We categorize videos based on the uploader into two types: (1) professionals, (2) General populations. Professionals include doctors, pharmacists, and other medical practitioners, while General populations include nutritionists and general populations (students and non-professional members of society). Based on content, videos are divided into three categories: (1) case sharing, (2) medication knowledge, (3) disease knowledge. Quality and Reliability Assessment Tools The blinded scoring was independently conducted by two researchers (Reviewer A and B) with backgrounds in hepatology or clinical pharmacy. Prior to scoring, they underwent standardized training on the scoring instruments and completed calibration exercises using 10 non-study sample videos to ensure a consistent understanding of the scoring criteria. The assessment was carried out using the following three tools: GQS: A 5-point Likert scale is used (1 = very poor quality, information severely lacking, of no use to patients; 5 = excellent quality, information complete and coherent, very useful to patients) to evaluate the overall quality and usefulness of information in the video [ 14 ]. For detailed information, see Table S1 . JAMA Benchmark Score: This assesses whether the video meets the four major benchmarks: authorship, citation of publishers, disclosure of information, and timeliness. One point is awarded for each criterion met, with a total score ranging from 0 to 4[ 1 ]. For more details, see Table S2. The mDISCERN comprises five questions concerning video reliability. Each 'Yes' response is scored 1 point, and each 'No' response is scored 0 points. Higher scores indicate greater reliability [ 6 ]. For more details, see Table S3. If there is a discrepancy between the two reviewers' scores (mDISCERN total score difference > 1 point, or GQS/JAMA score difference > 1 point), a third senior researcher will act as an arbitrator to determine the final score. The consistency between the two reviewers will be evaluated using Cohen's κ coefficient. The interpretation of κ values is as follows: 0.80 indicates strong agreement [ 7 , 15 ]. Statistical Analysis As the rating data did not meet the requirements for parametric testing, the data were summarized using medians and interquartile ranges (IQR). The Mann–Whitney U test and Wilcoxon Rank-Sum Test was used for non-parametric comparisons between two independent groups, while the Kruskal–Wallis H test was used for comparisons among three or more groups. Furthermore, we conducted Spearman correlation analysis to assess the relationship between general information (such as likes, favorites, shares, and video length) and quality/reliability scores. Statistical significance was set at p < 0.05. Data analysis was performed using SPSS Statistics version 27.0. Results Video characteristics and uploader types We retrieved 200 videos through keyword search for data extraction and analysis: 100 from TikTok and 100 from BiliBili. The general characteristics of the videos are shown in Table 1 , indicating that the numbers of likes, favorites, and shares of TikTok videos were higher than those of BiliBili ( p < 0.001), whereas the video duration and days since publication of BiliBili videos were higher than those of TikTok ( p < 0.001). Table 1 Comparison of Basic Characteristics of Videos Across Different Platforms Variable TikTok(n = 100)Median(IQR) BiliBili(n = 100)Median(IQR) Wilcoxon Rank-Sum Test p value Likes 505(86–2345) 11(3-108) -8.54 < 0.001 Favorites 170(33–815) 15(1–97) -6.113 < 0.001 Shares 169(27–848) 5(1–40) -7.736 < 0.001 Days Since Posting (days) 365(180–547) 730(365–1095) -4.178 < 0.001 Duration (s) 70(50–118) 107(70–494) -4.303 < 0.001 Figure 2 show the publishers and content of DILI-related videos on TikTok and BiliBili. On TikTok, a high proportion of videos were uploaded by professionals (93/100, 93%), while the remaining videos were uploaded by General populations (7/100, 7%). On BiliBili, including professionals (66/100, 66%), and General populations (34/100, 34%). Both platforms are devoted to disseminating scientific knowledge about diseases and medication. More than half of the 200 videos mentioned drugs or health supplements that can cause liver injury (107/200, 53.5%). Chemical drugs appeared most frequently, including antibiotics (n = 33), anti-inflammatory drugs (n = 28), chemotherapeutic agents (n = 15), and other chemical drugs (n = 22). Next were traditional Chinese medicines (n = 40), with He Shou Wu (n = 8) and Sanqi (n = 7) being mentioned most often. Finally, combination drug therapy was mentioned in 7 videos (n = 7). Comparison of Video Features from Different Publishers and Content The Table 2 and Table 3 results showed that there were significant differences in the video duration ( p < 0.001) among videos from different publishers, while there were no significant differences in the number of likes, favorites, shares, or days since publication. Regarding videos with different content, those providing medication knowledge received more likes ( p < 0.001), case sharing videos had higher numbers of favorites and shares ( p < 0.001), whereas there were no significant differences in days since publication or video duration. Table 2 Comparison of Video Features from Different publishers Variable General populations(n = 41)median(IQR) professionals(n = 159)median(IQR) Mann-Whitney U test p value Likes 40(6–289) 121(11–852) -1.624 0.104 Favorites 36(7-124) 57(7-373) -0.904 0.366 Shares 23(3-120) 40(3-251) -0.835 0.404 Days Since Posting (days) 607(399–968) 561(184–991) -0.639 0.523 Duration (s) 159(75–640) 81(52–134) -3.214 < .001 Table 3 Comparison of Video Features with Different Content Variable Case Sharing(n = 28)Median(IQR) Disease Knowledge(n = 95)Median(IQR) Medication Knowledge(n = 77) Median(IQR) Kruskal–Wallis H p value Likes 215(622–3951) 28(5-260) 249(33-1365) 17.520 < 0.001 Favorites 160(13–945) 24(4-101) 126(19–693) 17.899 < 0.001 Shares 161(11-1795) 11(1–90) 65(7-456) 19.606 < 0.001 Days Since Posting (days) 694(205–1107) 540(250–857) 686(178–1046) 0.752 0.687 Duration (s) 93(47–159) 89(52–199) 87(61–132) 0.445 0.801 Evaluation of Video Quality and Reliability The κ value was calculated to be 0.752, indicating strong agreement between the two raters. As shown in Table 4 – 6 and Fig. 3 , when comparing the quality of videos from different apps, all three scores for TikTok were significantly higher than those for BiliBili ( p < 0.001). For videos from different publishers, there were no significant differences in mDISCERN and GQS scores, while the JAMA score from professionals was significantly higher than that from General populations ( p < 0.05). Regarding videos of different content, all three scores showed significant differences, the mDISCERN score for medication knowledge was higher than that for case sharing and disease knowledge ( p < 0.05), the GQS score for disease knowledge was higher than that for case sharing and medication knowledge ( p < 0.05), and the JAMA score for case sharing was lower than that for disease knowledge and medication knowledge ( p < 0.05). Table 4 Analysis of Ratings Across Different Short Video Platforms Variables BiliBili(n = 100) TikTok(n = 100) Mann-Whitney U test p value mDISCERN score 3(2–3) 3(2–4) -3.286 < 0.001 GQS score 3(3–3) 4(4–4) -6.102 < 0.001 JAMA score 3(3–3) 3(3–4) -5.351 < 0.001 Table 5 Analysis of Ratings from Different Video Bublishers Variable General populations(n = 41)Median(IQR) professionals(n = 159)Median(IQR) Mann-Whitney U test p value mDISCERN score 3(2–3) 3(2–3) -0.513 0.608 GQS score 3(3–4) 4(3–4) -1.322 0.186 JAMA score 3(2–3) 3(3–4) -2.801 < 0.05 Table 6 Analysis of Ratings for Different Video Content Variable Case Sharing(n = 28)Median(IQR) Disease Knowledge(n = 95) Median(IQR) Medication Knowledge (n = 77) Median(IQR) Kruskal–Wallis H p value mDISCERN score 3(2–3) 3(2–3) 3(3–4) 7.067 <0.05 GQS score 3(3–4) 4(3–4) 3(3–4) 7.099 <0.05 JAMA score 3(2–3) 3(3–4) 3(3–4) 10.630 <0.05 Analysis of the Correlation Between Quality Scores and Audience Engagement Spearman correlation analysis (Fig. 4 ) showed that among user interaction metrics, the number of likes, favorites, and shares were highly positively correlated with each other (r = 0.734–0.940, p < 0.01), and all were significantly positively correlated with DISCERN scores (likes: r = 0.204, p = 0.004; favorites: r = 0.191, p = 0.007, shares: r = 0.156, p = 0.028), indicating a certain association between user interaction behaviors and content quality. In terms of quality assessment tools, DISCERN showed a significant positive correlation with both GQS (r = 0.260, p < 0.01) and JAMA (r = 0.263, p < 0.01), while the correlation between GQS and JAMA was relatively strong (r = 0.707, p < 0.01), indicating a certain consistency among different quality assessment tools. Overall, user interaction behaviors are closely interconnected and show a positive correlation with certain quality scores, while video duration and release time have little impact on other variables. Discussion This study is the first to systematically evaluate the information quality and reliability of content related to DILI on two major Chinese short video platforms, TikTok and BiliBili. Although these platforms currently offer high accessibility to a vast amount of health information and facilitate rapid dissemination, this study reveals that DILI videos differ in information quality across platforms, publishers, and content types, and that user interaction behaviors are associated with certain aspects of quality evaluation. Our analysis indicates that TikTok videos score significantly higher than BiliBili in terms across mDISCERN, GQS, and JAMA assessments ( p <0.001). This aligns with other cross-platform studies[ 18 ]. This suggests that TikTok, due to its strong algorithmic recommendations and high user engagement, may be more inclined to promote easily understandable short content, resulting in higher overall scores. However, overly simplified information may lead the public to develop a one-sided perception of risk, such as excessive fear of a specific drug or neglect of rare but high-risk factors. However, BiliBili videos are significantly longer, potentially encompassing more in-depth information on systematic pathological mechanisms, diagnostic criteria, and similar content, suggesting that platform characteristics have a profound impact on the presentation style and information density. Although TikTok receives higher ratings, the shortcoming of oversimplified content should not be overlooked, particularly for diseases such as DILI, which have complex etiologies and require careful differentiation, as concise and fast-paced content may fail to convey necessary warnings and special considerations. In addition, the source of the video and the type of content affect its quality, with videos released by professionals scoring higher on the JAMA scale( p <0.05). Content related to “medication knowledge” has the highest mDISCERN scores, which may be because it focuses more on presenting known, literature-supported drug risks (such as the frequently mentioned antibiotics and nonsteroidal anti-inflammatory drugs in this study)[ 11 , 5 ]. “Disease knowledge” videos scored higher on the GQS, likely because they more comprehensively cover prevention, symptoms, and treatment. “Case sharing” content received higher numbers of favorites and shares, indicating strong narrative appeal and emotional resonance, but scored lowest on the JAMA scale, suggesting that such content is often unclear regarding author qualifications, citation of evidence, and disclosure of conflicts of interest. While case narratives can effectively enhance public awareness, they may also lead to overgeneralized misunderstandings or the spread of unverified individual cases. In addition, there is a high degree of synergy among user interactions such as likes, favorites, and shares, and all of these are weakly positively correlated with the mDISCERN score. This suggests that, to some extent, users are more inclined to interact with content they perceive as reliable. However, this correlation is relatively weak (r: 0.156–0.204), indicating that the factors promoting user interactions are multidimensional and complex. Beyond the quality of the information itself, these factors also include the narrative techniques of the video, emotional appeal, visual effects, creator charisma, and the recommendation weight assigned by platform algorithms. Therefore, high interaction data cannot be simply equated with high-quality health information. In all the videos, more than half (53.5%) mentioned specific pathogenic drugs, among which antibiotics, antipyretic and anti-inflammatory analgesics, traditional Chinese medicines (especially He Shou Wu and Sanqi), as well as combination therapies, are all supported by a substantial amount of literature[ 16 , 8 , 9 ]. However, some videos merely list suspicious drugs without explaining their risk probabilities, conditions of occurrence (such as dosage, treatment duration, or combination therapy), or monitoring methods, which may trigger unnecessary fear of medication. Conversely, drugs that are overly emphasized and reported may lead the public to overestimate their risks, making them prone to overlook other equally important but less well-known risk factors. Although we have analyzed video quality and content information as thoroughly as possible, this study still has certain limitations. First, the study adopts a cross-sectional design, which can only capture the state of content on the platform at a specific point in time. The short video ecosystem is characterized by rapid updates and dynamic algorithm adjustments, meaning that the ranking, content, and even the existence of popular videos can change quickly. Therefore, the results of this study reflect a snapshot at a specific moment, and their representativeness may change over time. Secondly, the data collection is based on the results of the platform's “comprehensive ranking”, aiming to include the content most likely to be accessed by users. However, this method inherently introduces unavoidable platform algorithm biases. The ranking mechanism is influenced by user interaction data and the platform's internal rules, which may result in certain high-quality content with lower engagement or newly published items being excluded, thereby affecting the representativeness of the sample. Third, the search strategy only used the core medical term “药物性肝损伤” as a keyword, which may not cover non-professional expressions commonly used by the public (such as “medication harming the liver”), potentially leading to incomplete retrieval and the omission of some relevant videos described in layman's terms. In addition, although we used validated scoring tools (GQS, JAMA, mDISCERN) and conducted inter-rater consistency checks (κ = 0.752), the quality assessment process still involves subjective judgment. While discrepancies were resolved through arbitration, the scoring results to some extent reflect the evaluators' judgment criteria based on specific clinical and pharmaceutical backgrounds, and subjective differences cannot be ignored. Conclusion Due to differences in platform logic, TikTok and BiliBili have given rise to science popularization content with varying forms and emphases on quality. Professionals have a clear advantage in terms of information quality, but content from General populations is often more competitive in terms of appeal. User interactions in videos reflect the effectiveness of information dissemination, but they cannot be considered a reliable indicator for judging the scientific quality of the information. The drugs related to DILI in the video content focus on antibiotics, antipyretic and analgesic medications, and certain traditional Chinese medicines. Although this reflects clinical focus on liver injury grugs, there may be an issue of oversimplification in the content. In an era where health communication is increasingly short-video oriented, enhancing the professional competence and narrative skills of content creators, especially professionals, is crucial to building an online health information environment that is both engaging and scientifically reliable. Declarations Funding This study was supported by the Fundamental Research Funds for the Central Universities (No. YG2023LC13), Science and Technology Commission of Shanghai Municipality (No.23430761100), Shanghai Pudong New Area Health Commission Public Health Research Project (No.PW2024D-03), Joint Program on Health Science & Technology Innovation of Hainan Province (YJ2025011). Conflicts of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics approval and informed consent statements This article does not contain any studies with human or animal participants. Guarantor Not applicable Contributions RG N: Writing - original draft, BY: Writing - review & editing, HW: Investigation, SY Q: Project administration, CH: Visualization, ZL L: Funding acquisition, Supervision. 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J Med Internet Res 25:e47210. https://doi.org/10.2196/47210 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviewers invited by journal 04 May, 2026 Editor assigned by journal 04 May, 2026 Submission checks completed at journal 04 May, 2026 First submitted to journal 03 May, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9603197","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638443787,"identity":"6bb4090d-22f7-44fa-af37-dba72d14c3c9","order_by":0,"name":"Ruo-gu Nie","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruo-gu","middleName":"","lastName":"Nie","suffix":""},{"id":638443788,"identity":"9ea9877e-07dc-49b8-8932-e7096e0560f5","order_by":1,"name":"Bing Yu","email":"","orcid":"","institution":"Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Yu","suffix":""},{"id":638443789,"identity":"7c4315c6-7b5e-4b9a-8862-ea7d686f4cb0","order_by":2,"name":"Han Wang","email":"","orcid":"","institution":"Panzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Wang","suffix":""},{"id":638443790,"identity":"13e53aa2-2855-473e-aad3-da79ce288693","order_by":3,"name":"Sheng-ying Qin","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Sheng-ying","middleName":"","lastName":"Qin","suffix":""},{"id":638443791,"identity":"bd11fe41-b319-461f-a548-ea40a06388b3","order_by":4,"name":"Cong Huai","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Huai","suffix":""},{"id":638443792,"identity":"fcbc32b3-45bf-4b3a-add2-6b0289fde6b3","order_by":5,"name":"Zhi-ling Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACPmYGNoaEAgkGBvbmgw8SKmoIa2EDazEAauE5lmzw4MwxIrSAkQGQJZFjJvmwhZkILezszx48MLDIk3dIMKtIbGBj4G/vTiDksHQDoMOKDQ8cSLuRuEOGQeLM2Q2EtByTAGpJ3NjYcOxG4hk2BgOJXEJaGNsgWpoZ2woS25iJ0cLMBtYyn42ZjYFILWwQLRt42JglEs4c4yHoF37+488kf1TUJc6f//7jxx8VNXL87b34tcCBwQEIzUOcchCQbyBe7SgYBaNgFIwwAABVYUGI8Vxu1wAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Zhi-ling","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-05-04 01:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9603197/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9603197/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109054335,"identity":"6b359874-08ac-48c3-a848-89cc23c8be56","added_by":"auto","created_at":"2026-05-12 07:22:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54344,"visible":true,"origin":"","legend":"\u003cp\u003eSearch strategy for short videos on drug induced liver injury.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9603197/v1/734f71ee015f5cc240921df8.png"},{"id":109067760,"identity":"e0baa7b4-b68d-4902-80c4-72c8fa7e2208","added_by":"auto","created_at":"2026-05-12 10:00:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93107,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The proportion of short videos on TikTok and BiliBili regarding drug-induced liver injury from different publishers and varying content. (B) Videos about drugs that cause liver damage. The right side of the graph shows the proportion of videos mentioning drugs related to liver injury. The left side shows the drugs related to liver injury explained in the video content.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9603197/v1/639023176afc40cb5599626d.png"},{"id":109054336,"identity":"1c82e8f5-02de-4c2e-89fb-041faa77b69f","added_by":"auto","created_at":"2026-05-12 07:22:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116761,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of video quality ratings. (A) Comparison of ratings across different platforms. (B) Comparison of ratings from different publishers. (C) Comparison of ratings for different types of content. The horizontal lines in the boxplots represent the median values. *represents\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.05, **represents \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***represents\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9603197/v1/aee6a257261e86c4117886ef.png"},{"id":109068175,"identity":"d16a9235-04a8-4380-8a2b-27680b3e3aab","added_by":"auto","created_at":"2026-05-12 10:04:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73175,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the Correlation between Video Quality Ratings and Audience Engagement.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9603197/v1/54adf16b20a07d36d2efbed3.png"},{"id":109204035,"identity":"66112fc5-cc1a-4a34-8d10-c500ad7530bf","added_by":"auto","created_at":"2026-05-13 14:52:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":619537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9603197/v1/519d8d5a-dfeb-46fe-baec-231f3bfbdf5d.pdf"},{"id":109067771,"identity":"395b37a0-09e4-48bb-93df-09f4db4610b6","added_by":"auto","created_at":"2026-05-12 10:00:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14332,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9603197/v1/9096c5b7e4dac19a3e470ae1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating the Quality and Reliability of DILI in Chinese Videos on TikTok and BiliBili: Cross-Sectional Content Analysis Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDrug-induced liver injury (DILI) refers to a heterogeneous group of liver diseases caused by various prescription and non-prescription chemical drugs, biological agents, traditional herbal medicines, dietary supplements, and health products[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. As one of the important drug-induced diseases and common causes of acute liver failure worldwide, the pathogenesis of DILI is complex, its clinical manifestations are diverse, and its diagnosis is highly challenging[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In China, due to the large population base, the wide variety of clinical medications, and the extensive use of traditional Chinese medicine and health supplements, the disease burden of DILI has become a public health issue that cannot be ignored in clinical practice[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, the prevention and early identification of DILI, as well as enhancing public awareness of DILI, have become crucial.\u003c/p\u003e \u003cp\u003eIn recent years, the deep integration of the Internet and digital media has fundamentally changed the way the public obtains and communicates health information. Social media, particularly video platforms represented by TikTok and BiliBili, have become one of the preferred channels for the public to seek medical knowledge and share illness experiences, thanks to their intuitive and vivid content presentation, rapid dissemination, and high user interactivity[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This shift from passive reception to active seeking has improved the efficiency of health information dissemination. However, the low entry threshold of short video platforms and their relatively lenient content review mechanisms allow any user to freely upload medical-related content, resulting in a wide variation in the quality of health information on these platforms. A large portion of the content may originate from General populations, posing risks of one-sided information, insufficient scientific basis, and even misleading commercial promotion, presenting significant challenges for controlling information quality[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, in specialized and complex fields such as DILI, where public awareness is relatively limited, low-quality or incorrect information may mislead patients into unnecessary fear of medication or cause them to overlook actual signs of liver injury, thereby delaying diagnosis and treatment and posing a threat to their health[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, some studies have begun using tools such as the Global Quality Score (GQS) and the modified DISCERN (mDISCERN) to evaluate the quality of popular science videos on platforms like YouTube and TikTok that focus on specific diseases, such as liver cancer and gallstones. These studies have found that content produced by professional healthcare practitioners is generally more reliable[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the quality of short videos on DILI has not yet been studied. Therefore, we conducted a systematic evaluation of the content and quality reliability of 200 Chinese short videos related to DILI on TikTok and BiliBili, two major Chinese short video platforms.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eEthical considerations\u003c/h2\u003e\n \u003cp\u003eThis study is an observational study based on publicly available online information. All data were obtained from publicly accessible content on short video platforms and did not involve any personal private information or interactions with users, therefore, ethics committee approval was not required.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSearch Strategy and Data Collection\u003c/h3\u003e\n\u003cp\u003eThis study is a cross-sectional study. On December 20, 2025, searches were conducted in the Chinese versions of the TikTok and BiliBili mobile applications using \u0026quot;药物性肝损伤\u0026quot; as the primary keyword. To minimize bias introduced by personalized recommendation algorithms, newly registered accounts were used for the searches.\u003c/p\u003e\n\u003cp\u003eFirst, exclude videos that do not meet the research criteria based on the exclusion standards, and then select the top 100 for analysis according to the comprehensive ranking of the video platform (including likes, comments, shares, etc). The exclusion criteria include: non-Chinese videos, duplicate videos (same content by different creators), and videos without a clear author or title, the detailed procedure is shown in Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Simultaneously record the basic information of the videos, including the video title, uploader identity (doctor, pharmacist, general communicator, hospital, association, etc.), video content category, drugs causing liver injury, video duration, release date, number of likes, shares, and favorites. All extracted data are recorded in Excel (Microsoft Corporation).\u003c/p\u003e\n\u003ch3\u003eVideo Classification\u003c/h3\u003e\n\u003cp\u003eWe categorize videos based on the uploader into two types: (1) professionals, (2) General populations. Professionals include doctors, pharmacists, and other medical practitioners, while General populations include nutritionists and general populations (students and non-professional members of society). Based on content, videos are divided into three categories: (1) case sharing, (2) medication knowledge, (3) disease knowledge.\u003c/p\u003e\n\u003ch3\u003eQuality and Reliability Assessment Tools\u003c/h3\u003e\n\u003cp\u003eThe blinded scoring was independently conducted by two researchers (Reviewer A and B) with backgrounds in hepatology or clinical pharmacy. Prior to scoring, they underwent standardized training on the scoring instruments and completed calibration exercises using 10 non-study sample videos to ensure a consistent understanding of the scoring criteria. The assessment was carried out using the following three tools:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eGQS: A 5-point Likert scale is used (1\u0026thinsp;=\u0026thinsp;very poor quality, information severely lacking, of no use to patients; 5\u0026thinsp;=\u0026thinsp;excellent quality, information complete and coherent, very useful to patients) to evaluate the overall quality and usefulness of information in the video [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For detailed information, see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eJAMA Benchmark Score: This assesses whether the video meets the four major benchmarks: authorship, citation of publishers, disclosure of information, and timeliness. One point is awarded for each criterion met, with a total score ranging from 0 to 4[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. For more details, see Table S2.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe mDISCERN comprises five questions concerning video reliability. Each \u0026apos;Yes\u0026apos; response is scored 1 point, and each \u0026apos;No\u0026apos; response is scored 0 points. Higher scores indicate greater reliability [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For more details, see Table S3.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIf there is a discrepancy between the two reviewers\u0026apos; scores (mDISCERN total score difference\u0026thinsp;\u0026gt;\u0026thinsp;1 point, or GQS/JAMA score difference\u0026thinsp;\u0026gt;\u0026thinsp;1 point), a third senior researcher will act as an arbitrator to determine the final score. The consistency between the two reviewers will be evaluated using Cohen\u0026apos;s \u0026kappa; coefficient. The interpretation of \u0026kappa; values is as follows: \u0026lt;0.20 indicates poor agreement, 0.21\u0026ndash;0.40 indicates fair agreement, 0.41\u0026ndash;0.60 indicates moderate agreement, 0.61\u0026ndash;0.80 indicates substantial agreement, and \u0026gt;\u0026thinsp;0.80 indicates strong agreement [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eAs the rating data did not meet the requirements for parametric testing, the data were summarized using medians and interquartile ranges (IQR). The Mann\u0026ndash;Whitney U test and Wilcoxon Rank-Sum Test was used for non-parametric comparisons between two independent groups, while the Kruskal\u0026ndash;Wallis H test was used for comparisons among three or more groups. Furthermore, we conducted Spearman correlation analysis to assess the relationship between general information (such as likes, favorites, shares, and video length) and quality/reliability scores. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Data analysis was performed using SPSS Statistics version 27.0.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003eVideo characteristics and uploader types\u003c/h2\u003e\n \u003cp\u003eWe retrieved 200 videos through keyword search for data extraction and analysis: 100 from TikTok and 100 from BiliBili. The general characteristics of the videos are shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, indicating that the numbers of likes, favorites, and shares of TikTok videos were higher than those of BiliBili (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas the video duration and days since publication of BiliBili videos were higher than those of TikTok (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of Basic Characteristics of Videos Across Different Platforms\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTikTok(n\u0026thinsp;=\u0026thinsp;100)Median(IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBiliBili(n\u0026thinsp;=\u0026thinsp;100)Median(IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eWilcoxon Rank-Sum Test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLikes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e505(86\u0026ndash;2345)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11(3-108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-8.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFavorites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e170(33\u0026ndash;815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15(1\u0026ndash;97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-6.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eShares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e169(27\u0026ndash;848)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5(1\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-7.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDays Since Posting (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e365(180\u0026ndash;547)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e730(365\u0026ndash;1095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-4.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDuration (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e70(50\u0026ndash;118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e107(70\u0026ndash;494)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-4.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the publishers and content of DILI-related videos on TikTok and BiliBili. On TikTok, a high proportion of videos were uploaded by professionals (93/100, 93%), while the remaining videos were uploaded by General populations (7/100, 7%). On BiliBili, including professionals (66/100, 66%), and General populations (34/100, 34%). Both platforms are devoted to disseminating scientific knowledge about diseases and medication. More than half of the 200 videos mentioned drugs or health supplements that can cause liver injury (107/200, 53.5%). Chemical drugs appeared most frequently, including antibiotics (n\u0026thinsp;=\u0026thinsp;33), anti-inflammatory drugs (n\u0026thinsp;=\u0026thinsp;28), chemotherapeutic agents (n\u0026thinsp;=\u0026thinsp;15), and other chemical drugs (n\u0026thinsp;=\u0026thinsp;22). Next were traditional Chinese medicines (n\u0026thinsp;=\u0026thinsp;40), with He Shou Wu (n\u0026thinsp;=\u0026thinsp;8) and Sanqi (n\u0026thinsp;=\u0026thinsp;7) being mentioned most often. Finally, combination drug therapy was mentioned in 7 videos (n\u0026thinsp;=\u0026thinsp;7).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eComparison of Video Features from Different Publishers and Content\u003c/h3\u003e\n\u003cp\u003eThe Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e results showed that there were significant differences in the video duration (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among videos from different publishers, while there were no significant differences in the number of likes, favorites, shares, or days since publication. Regarding videos with different content, those providing medication knowledge received more likes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), case sharing videos had higher numbers of favorites and shares (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas there were no significant differences in days since publication or video duration.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of Video Features from Different publishers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eGeneral populations(n\u0026thinsp;=\u0026thinsp;41)median(IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eprofessionals(n\u0026thinsp;=\u0026thinsp;159)median(IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMann-Whitney U test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLikes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e40(6\u0026ndash;289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e121(11\u0026ndash;852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-1.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFavorites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e36(7-124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e57(7-373)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eShares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e23(3-120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e40(3-251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDays Since Posting (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e607(399\u0026ndash;968)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e561(184\u0026ndash;991)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDuration (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e159(75\u0026ndash;640)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e81(52\u0026ndash;134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-3.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of Video Features with Different Content\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCase Sharing(n\u0026thinsp;=\u0026thinsp;28)Median(IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDisease Knowledge(n\u0026thinsp;=\u0026thinsp;95)Median(IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMedication Knowledge(n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\n \u003cp\u003eMedian(IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eKruskal\u0026ndash;Wallis H\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLikes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e215(622\u0026ndash;3951)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e28(5-260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e249(33-1365)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e17.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFavorites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e160(13\u0026ndash;945)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e24(4-101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e126(19\u0026ndash;693)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e17.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eShares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e161(11-1795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11(1\u0026ndash;90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e65(7-456)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e19.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDays Since Posting (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e694(205\u0026ndash;1107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e540(250\u0026ndash;857)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e686(178\u0026ndash;1046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDuration (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e93(47\u0026ndash;159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e89(52\u0026ndash;199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e87(61\u0026ndash;132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eEvaluation of Video Quality and Reliability\u003c/h2\u003e\n \u003cp\u003eThe \u0026kappa; value was calculated to be 0.752, indicating strong agreement between the two raters. As shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, when comparing the quality of videos from different apps, all three scores for TikTok were significantly higher than those for BiliBili (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For videos from different publishers, there were no significant differences in mDISCERN and GQS scores, while the JAMA score from professionals was significantly higher than that from General populations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Regarding videos of different content, all three scores showed significant differences, the mDISCERN score for medication knowledge was higher than that for case sharing and disease knowledge (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the GQS score for disease knowledge was higher than that for case sharing and medication knowledge (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the JAMA score for case sharing was lower than that for disease knowledge and medication knowledge (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of Ratings Across Different Short Video Platforms\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eBiliBili(n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eTikTok(n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMann-Whitney U test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emDISCERN score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3(2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3(2\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-3.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGQS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3(3\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4(4\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-6.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eJAMA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3(3\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3(3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-5.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of Ratings from Different Video Bublishers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eGeneral populations(n\u0026thinsp;=\u0026thinsp;41)Median(IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eprofessionals(n\u0026thinsp;=\u0026thinsp;159)Median(IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMann-Whitney U test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emDISCERN score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3(2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3(2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGQS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3(3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4(3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-1.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eJAMA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3(2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3(3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-2.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of Ratings for Different Video Content\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCase Sharing(n\u0026thinsp;=\u0026thinsp;28)Median(IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDisease Knowledge(n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e\n \u003cp\u003eMedian(IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMedication Knowledge\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\n \u003cp\u003eMedian(IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003eKruskal\u0026ndash;Wallis H\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emDISCERN score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3(2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3(2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e3(3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGQS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3(3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4(3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e3(3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eJAMA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3(2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3(3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e3(3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e10.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis of the Correlation Between Quality Scores and Audience Engagement\u003c/h2\u003e\n \u003cp\u003eSpearman correlation analysis (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that among user interaction metrics, the number of likes, favorites, and shares were highly positively correlated with each other (r\u0026thinsp;=\u0026thinsp;0.734\u0026ndash;0.940, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and all were significantly positively correlated with DISCERN scores (likes: r\u0026thinsp;=\u0026thinsp;0.204, p\u0026thinsp;=\u0026thinsp;0.004; favorites: r\u0026thinsp;=\u0026thinsp;0.191, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, shares: r\u0026thinsp;=\u0026thinsp;0.156, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), indicating a certain association between user interaction behaviors and content quality.\u003c/p\u003e\n \u003cp\u003eIn terms of quality assessment tools, DISCERN showed a significant positive correlation with both GQS (r\u0026thinsp;=\u0026thinsp;0.260, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and JAMA (r\u0026thinsp;=\u0026thinsp;0.263, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the correlation between GQS and JAMA was relatively strong (r\u0026thinsp;=\u0026thinsp;0.707, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating a certain consistency among different quality assessment tools.\u003c/p\u003e\n \u003cp\u003eOverall, user interaction behaviors are closely interconnected and show a positive correlation with certain quality scores, while video duration and release time have little impact on other variables.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to systematically evaluate the information quality and reliability of content related to DILI on two major Chinese short video platforms, TikTok and BiliBili. Although these platforms currently offer high accessibility to a vast amount of health information and facilitate rapid dissemination, this study reveals that DILI videos differ in information quality across platforms, publishers, and content types, and that user interaction behaviors are associated with certain aspects of quality evaluation.\u003c/p\u003e \u003cp\u003eOur analysis indicates that TikTok videos score significantly higher than BiliBili in terms across mDISCERN, GQS, and JAMA assessments (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). This aligns with other cross-platform studies[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This suggests that TikTok, due to its strong algorithmic recommendations and high user engagement, may be more inclined to promote easily understandable short content, resulting in higher overall scores. However, overly simplified information may lead the public to develop a one-sided perception of risk, such as excessive fear of a specific drug or neglect of rare but high-risk factors.\u003c/p\u003e \u003cp\u003eHowever, BiliBili videos are significantly longer, potentially encompassing more in-depth information on systematic pathological mechanisms, diagnostic criteria, and similar content, suggesting that platform characteristics have a profound impact on the presentation style and information density. Although TikTok receives higher ratings, the shortcoming of oversimplified content should not be overlooked, particularly for diseases such as DILI, which have complex etiologies and require careful differentiation, as concise and fast-paced content may fail to convey necessary warnings and special considerations. In addition, the source of the video and the type of content affect its quality, with videos released by professionals scoring higher on the JAMA scale(\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). Content related to \u0026ldquo;medication knowledge\u0026rdquo; has the highest mDISCERN scores, which may be because it focuses more on presenting known, literature-supported drug risks (such as the frequently mentioned antibiotics and nonsteroidal anti-inflammatory drugs in this study)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. \u0026ldquo;Disease knowledge\u0026rdquo; videos scored higher on the GQS, likely because they more comprehensively cover prevention, symptoms, and treatment. \u0026ldquo;Case sharing\u0026rdquo; content received higher numbers of favorites and shares, indicating strong narrative appeal and emotional resonance, but scored lowest on the JAMA scale, suggesting that such content is often unclear regarding author qualifications, citation of evidence, and disclosure of conflicts of interest. While case narratives can effectively enhance public awareness, they may also lead to overgeneralized misunderstandings or the spread of unverified individual cases.\u003c/p\u003e \u003cp\u003eIn addition, there is a high degree of synergy among user interactions such as likes, favorites, and shares, and all of these are weakly positively correlated with the mDISCERN score. This suggests that, to some extent, users are more inclined to interact with content they perceive as reliable. However, this correlation is relatively weak (r: 0.156\u0026ndash;0.204), indicating that the factors promoting user interactions are multidimensional and complex. Beyond the quality of the information itself, these factors also include the narrative techniques of the video, emotional appeal, visual effects, creator charisma, and the recommendation weight assigned by platform algorithms. Therefore, high interaction data cannot be simply equated with high-quality health information.\u003c/p\u003e \u003cp\u003eIn all the videos, more than half (53.5%) mentioned specific pathogenic drugs, among which antibiotics, antipyretic and anti-inflammatory analgesics, traditional Chinese medicines (especially He Shou Wu and Sanqi), as well as combination therapies, are all supported by a substantial amount of literature[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, some videos merely list suspicious drugs without explaining their risk probabilities, conditions of occurrence (such as dosage, treatment duration, or combination therapy), or monitoring methods, which may trigger unnecessary fear of medication. Conversely, drugs that are overly emphasized and reported may lead the public to overestimate their risks, making them prone to overlook other equally important but less well-known risk factors.\u003c/p\u003e \u003cp\u003eAlthough we have analyzed video quality and content information as thoroughly as possible, this study still has certain limitations. First, the study adopts a cross-sectional design, which can only capture the state of content on the platform at a specific point in time. The short video ecosystem is characterized by rapid updates and dynamic algorithm adjustments, meaning that the ranking, content, and even the existence of popular videos can change quickly. Therefore, the results of this study reflect a snapshot at a specific moment, and their representativeness may change over time. Secondly, the data collection is based on the results of the platform's \u0026ldquo;comprehensive ranking\u0026rdquo;, aiming to include the content most likely to be accessed by users. However, this method inherently introduces unavoidable platform algorithm biases. The ranking mechanism is influenced by user interaction data and the platform's internal rules, which may result in certain high-quality content with lower engagement or newly published items being excluded, thereby affecting the representativeness of the sample. Third, the search strategy only used the core medical term \u0026ldquo;药物性肝损伤\u0026rdquo; as a keyword, which may not cover non-professional expressions commonly used by the public (such as \u0026ldquo;medication harming the liver\u0026rdquo;), potentially leading to incomplete retrieval and the omission of some relevant videos described in layman's terms. In addition, although we used validated scoring tools (GQS, JAMA, mDISCERN) and conducted inter-rater consistency checks (κ\u0026thinsp;=\u0026thinsp;0.752), the quality assessment process still involves subjective judgment. While discrepancies were resolved through arbitration, the scoring results to some extent reflect the evaluators' judgment criteria based on specific clinical and pharmaceutical backgrounds, and subjective differences cannot be ignored.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDue to differences in platform logic, TikTok and BiliBili have given rise to science popularization content with varying forms and emphases on quality. Professionals have a clear advantage in terms of information quality, but content from General populations is often more competitive in terms of appeal. User interactions in videos reflect the effectiveness of information dissemination, but they cannot be considered a reliable indicator for judging the scientific quality of the information. The drugs related to DILI in the video content focus on antibiotics, antipyretic and analgesic medications, and certain traditional Chinese medicines. Although this reflects clinical focus on liver injury grugs, there may be an issue of oversimplification in the content. In an era where health communication is increasingly short-video oriented, enhancing the professional competence and narrative skills of content creators, especially professionals, is crucial to building an online health information environment that is both engaging and scientifically reliable.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Fundamental Research Funds for the Central Universities (No. YG2023LC13), Science and Technology Commission of Shanghai Municipality (No.23430761100), Shanghai Pudong New Area Health Commission Public Health Research Project (No.PW2024D-03), Joint Program on Health Science \u0026amp; Technology Innovation of Hainan Province (YJ2025011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and informed consent statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human or animal participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuarantor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRG N:\u0026nbsp;\u003c/strong\u003eWriting - original draft, \u003cstrong\u003eBY:\u003c/strong\u003e Writing - review \u0026amp; editing, \u003cstrong\u003eHW:\u003c/strong\u003e Investigation, \u003cstrong\u003eSY Q:\u003c/strong\u003e Project administration, \u003cstrong\u003eCH:\u003c/strong\u003eVisualization, \u003cstrong\u003eZL L:\u003c/strong\u003e Funding acquisition, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArslan B, Arslan E Evaluation of the quality, accuracy, and reliability of YouTube\u0026trade; videos on laminate veneer treatment in Turkish. 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J Med Internet Res 25:e47210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/47210\u003c/span\u003e\u003cspan address=\"10.2196/47210\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Drug induced liver injury, TikTok, BiliBili, Information quality, Short videos","lastPublishedDoi":"10.21203/rs.3.rs-9603197/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9603197/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study is a cross-sectional analysis aimed at systematically evaluating the information quality and reliability of popular science content related to drug induced liver injury (DILI) on the two major Chinese video platforms, TikTok and BiliBili, and analyzing its content characteristics.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOn December 2025, obtained drug-induced liver injury-related video information on two platforms. Exclude content that does not meet the requirements based on the exclusion criteria, and ultimately retain the top 100 videos from each platform that meet the standards for analysis(N\u0026thinsp;=\u0026thinsp;200). Two trained reviewers independently performed blinded assessments using the Global Quality Score (GQS), Journal of the American Medical Association (JAMA) benchmark criteria, and the modified DISCERN (mDISCERN) tool. Non-parametric tests were used to compare differences between groups, and Spearman correlation analysis was employed to explore the relationship between video characteristics and quality scores.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUser engagement metrics (likes, favorites, shares) for TikTok videos were significantly higher than those for BiliBili (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). In terms of information quality, TikTok videos scored significantly higher than BiliBili on the GQS, JAMA, and mDISCERN scales (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). There were differences in quality across content types: videos on \u0026ldquo;medication knowledge\u0026rdquo; received the highest mDISCERN reliability scores, \u0026ldquo;disease knowledge\u0026rdquo; videos scored higher in GQS practicality. Correlation analysis showed a weak positive correlation between user engagement metrics and mDISCERN scores. Among the 107 videos mentioning liver injury related drugs, chemical drugs (antibacterial, chemotherapeutic, and anti-inflammatory drugs) and traditional Chinese medicines (such as He Shou Wu) were mentioned most frequently.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe quality of DILI related content on short video platforms is related to platform algorithms, creator identity, and content type. High user engagement is not a reliable indicator of high-quality information. While current content spreads known drug risks, it tends to simplify information and focus on popular topics.\u003c/p\u003e","manuscriptTitle":"Evaluating the Quality and Reliability of DILI in Chinese Videos on TikTok and BiliBili: Cross-Sectional Content Analysis Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 07:22:46","doi":"10.21203/rs.3.rs-9603197/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T19:38:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37371419900812173594099523285900915451","date":"2026-05-18T17:13:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-04T09:39:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T07:08:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-04T07:07:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Naunyn-Schmiedeberg's Archives of Pharmacology","date":"2026-05-04T01:46:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7631bf1e-af5a-46bc-920b-8c07d2443226","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T19:38:33+00:00","index":77,"fulltext":""},{"type":"reviewerAgreed","content":"37371419900812173594099523285900915451","date":"2026-05-18T17:13:03+00:00","index":74,"fulltext":""},{"type":"reviewersInvited","content":"61","date":"2026-05-04T09:39:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T07:08:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-04T07:07:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Naunyn-Schmiedeberg's Archives of Pharmacology","date":"2026-05-04T01:46:51+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T07:22:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 07:22:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9603197","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9603197","identity":"rs-9603197","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}