Popularity Does Not Reflect Quality: A Cross-Platform Assessment of Pulmonary Embolism Health Information on Chinese Video Platforms

Popularity Does Not Reflect Quality: A Cross-Platform Assessment of Pulmonary Embolism Health Information on Chinese Video Platforms | 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 Article Popularity Does Not Reflect Quality: A Cross-Platform Assessment of Pulmonary Embolism Health Information on Chinese Video Platforms Ning Li, Lilin Deng, Jinxia Wu, Junhe Huang, Guojun Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8864402/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Pulmonary embolism (PE)-related videos are widely consumed as online health information, yet user engagement may not reflect informational quality and reliability. We evaluated 456 PE-related videos from TikTok (n = 205), Bilibili (n = 142), and RedNote (n = 109). Video characteristics, publisher type, engagement metrics, and content features were extracted. Quality and reliability were assessed using the Global Quality Score (GQS) and modified DISCERN (mDISCERN). Significant platform differences were observed in engagement patterns, publisher composition, content distribution, and quality scores. Only 33.3% of videos mentioned venous thromboembolism (VTE) prophylaxis. Engagement metrics were strongly intercorrelated but showed weak associations with GQS and mDISCERN. Ordinal logistic regression identified publisher type as a strong independent predictor of quality. Videos produced by medical organizations were more likely to achieve higher GQS (OR = 4.69, 95% CI 2.10–10.46) and mDISCERN scores (OR = 2.55, 95% CI 1.10–5.92), whereas videos not mentioning VTE were less likely to receive high scores. These findings indicate substantial heterogeneity in PE-related videos. They also suggest that highly disseminated content may not always be evidence-based, highlighting the importance of greater institutional participation and guideline-oriented prevention messaging. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Pulmonary embolism Venous thromboembolism Video platforms Health information quality mDISCERN Global Quality Score Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Venous thromboembolism (VTE) is a complex disorder encompassing deep vein thrombosis (DVT) and its most dangerous complication, pulmonary embolism (PE) [ 1 ]. PE is a potentially fatal condition that occurs when a thrombus originating in the venous circulation dislodges and embolizes to the pulmonary arterial system [ 2 ]. Numerous risk factors have been implicated for PE, including surgery, malignancy, advanced age, and obesity, all of which are commonly encountered in modern medical settings [ 3 ]. At present, PE remains a major contributor to the global disease burden, and its reported incidence appears to be increasing worldwide [ 4 , 5 ]. Despite substantial advances in diagnostic techniques and therapeutic strategies, PE continues to pose significant challenges in timely diagnosis and optimal management [ 6 ]. Notably, although PE is recognized as the third most frequent acute cardiovascular disease globally, it remains comparatively under-recognized when contrasted with myocardial infarction and stroke [ 7 , 8 ]. Consequently, improving public awareness of PE risk factors, early warning signs, and fundamental treatment principles may represent an important public health priority. Such efforts are essential for mitigating PE-related morbidity and mortality [ 2 ]. In recent years, social media platforms have rapidly evolved into major channels for the dissemination of health-related information. This shift has reshaped how the public accesses and interprets medical knowledge [ 9 ]. Video platforms, in particular, offer visually engaging and easily accessible formats that facilitate the efficient communication of complex medical concepts to broad audiences [ 10 ]. With the growing demand for accessible health education, video-based media have emerged as a promising approach for disseminating educational information related to pulmonary embolism [ 11 ]. In China, Douyin (TikTok), Bilibili, and Rednote are among the most influential and widely used video platforms. These platforms differ substantially in content format and audience profile [ 12 , 13 ]. However, amid information overload and the spread of misinformation, concerns have been raised regarding the quality and reliability of health-related videos on social media [ 9 , 14 , 15 ]. Previous studies have evaluated health-related video content across a range of disease topics, including cancer, chronic conditions, and infectious diseases [ 16 – 18 ]. Nevertheless, their findings have varied considerably across platforms and disease domains. Despite the increasing presence of PE–related content on social media, its quality and educational value remain insufficiently evaluated. Previous studies have mainly focused on text-based patient information and have reported substantial deficiencies in the quality, usability, and readability of pulmonary embolism–related materials [ 19 ]. In contrast, video-based content on social media has not been systematically assessed. Given the algorithm-driven nature of short-video platforms, highly disseminated health information may not necessarily be evidence-based. In this context, evaluating both the quality and reliability of PE-related video content and identifying factors associated with higher-quality information are essential for improving public health communication. Therefore, this study aimed to evaluate PE videos on TikTok, Bilibili, and Rednote with respect to content quality and reliability. The findings are expected to inform future digital health communication strategies and contribute to improved public education on pulmonary embolism. 2 Methods 2.1 Study design This study employed a cross-sectional design to evaluate pulmonary embolism–related videos on major Chinese video-sharing platforms, including TikTok (Chinese version: Douyin), Bilibili, and Rednote. 2.2 Ethical considerations All data analyzed in this study were derived from publicly accessible videos on TikTok, Bilibili, and Rednote. Ethical approval was not required because only publicly available data were analyzed and no identifiable information was collected. 2.3 Search strategy and data extraction A systematic search was conducted on TikTok, Bilibili, and Rednote using the Chinese keyword “肺栓塞” (pulmonary embolism). A standardized medical term was deliberately used to ensure consistency and reproducibility of the search strategy across platforms. All searches were performed on the same day (January 9, 2026) to minimize temporal variation. To reduce potential bias arising from personalized recommendation algorithms, all user accounts were logged out prior to the searches. Browser caches and search histories were cleared, and newly registered accounts were created to ensure neutral baseline conditions. Search results were displayed using each platform’s default sorting option without applying additional filters. Videos were screened sequentially from the highest-ranked to lower-ranked results according to the platform-generated ranking. Videos were excluded if they met any of the following criteria: (1) duplicate content, (2) irrelevant content not related to pulmonary embolism, or (3) content primarily intended for advertising or commercial promotion. For duplicate videos originating from the same source, the version uploaded by a verified account was retained; if no verified account was available, the earliest uploaded video was selected. No restrictions were applied regarding publication year, video duration, or language. The detailed video selection process is presented in Fig. 1 . For each eligible video, the following information was extracted and recorded: platform, title, URL, video duration, numbers of likes, comments, shares, and collections, types of publishers, as well as content category. 2.4 Classification of Videos We divided the videos into 4 groups according to the source and into 7 groups according to the content. Video sources were categorized as follows: (1) medical professionals, (2) non-medical professionals, (3) medical organizations and (4) non-medical organizations. Video content was classified into the following categories: (1) disease definition, (2) epidemiology, (3) pathological mechanisms, (4) diagnostic criteria, (5) risk factors, (6) preventive strategies, and (7) therapeutic strategies. Individual videos may encompass content spanning multiple categories. Regarding videos from preventive strategies, further classification was performed as follows: (1) basic prophylaxis, (2) mechanical prophylaxis, and (3) pharmacological prophylaxis. Data extraction was performed independently by two reviewers within a 48-hour period to minimize temporal bias. Any discrepancies were resolved through discussion, and a third reviewer was consulted when consensus could not be reached. Specific classification criteria are shown in Supplementary Material. 2.5 Video quality and reliability assessment Between January 10 and January 12, 2026, two physicians specializing in respiratory medicine conducted quality and reliability assessments using two validated instruments: the Global Quality Score (GQS) and the modified DISCERN (mDISCERN). The GQS is a widely used tool for evaluating overall quality of health information in videos, assessing aspects such as quality, fluency, comprehensiveness, and usefulness to patients [ 20 ]. The score ranges from 1 (poor quality) to 5 (excellent overall quality). Video reliability was assessed using the mDISCERN instrument [ 21 ]. The mDISCERN tool consists of five dichotomous items evaluating the reliability of health information, including: (1) whether the video is clear, concise, and understandable; (2) whether valid sources are cited; (3) whether the content is presented in a balanced and unbiased manner; (4) whether additional sources are provided for further reference; and (5) whether areas of uncertainty are mentioned [ 22 , 23 ]. Each item was scored as “yes” (1 point) or “no” (0 points), yielding a total score ranging from 0 to 5, with higher scores indicating greater reliability. Detailed scoring criteria for both the GQS and mDISCERN instruments are provided in the Supplementary Material. To ensure rating consistency, both reviewers underwent standardized training prior to formal assessment. Videos were divided into two groups and evaluated using a cross-rating approach. When discrepancies occurred, the final score was calculated as the mean of the two ratings. Inter-rater agreement was assessed using Cohen’s κ statistic. According to the criteria proposed by Landis and Koch, κ values > 0.80 indicate almost perfect agreement. In this study, inter-rater agreement was high for both GQS (κ = 0.828) and mDISCERN (κ = 0.869), indicating excellent consistency between reviewers. To minimize potential bias related to video popularity, reviewers were blinded to engagement-related metrics, including the number of likes, comments, shares, and collections, during the assessment process. 2.6 Statistical analysis Statistical analyses were performed using Python (version 3.10). Normality was assessed using the Shapiro-Wilk test. Continuous variables are presented as mean (standard deviation) or median (interquartile range), as appropriate. Group comparisons were conducted using the Student t test, Mann-Whitney U test, one-way ANOVA, or Kruskal-Wallis test. Post-hoc pairwise comparisons were performed using Dunn’s test with Holm-Bonferroni correction for multiple comparisons when Kruskal-Wallis tests indicated statistical significance. Spearman correlation analysis was used to assess associations among video characteristics, engagement metrics, and quality scores. To integrate multiple engagement metrics and reduce multicollinearity, principal component analysis (PCA) was applied, and the first principal component was extracted as the video interaction index. A two-sided P value < 0.05 was considered statistically significant. 3 Results 3.1 General characteristics of videos As shown in Table 1 , a total of 456 videos about pulmonary embolism were included for further analysis, including 205 videos from TikTok, 142 from Bilibili, and 109 from RedNote. The numbers of likes, collections, comments, and shares differed significantly among the three platforms, with TikTok showing the highest engagement (all P < 0.001). In contrast, video duration was significantly longer on Bilibili than on TikTok and RedNote ( P < 0.001). Table 1 Characteristics, quality, and reliability scores of videos about pulmonary embolism on TikTok/Bilibili/Rednote Variables Total (n = 456) TikTok (n = 205) Bilibili (n = 142) RedNote (n = 109) P-value H/χ 2 General information Likes, M (Q1, Q3) 50.50 (10.00, 262.75) 161.00 (61.00, 872.00) 4.00 (1.00, 19.00) 29.00 (15.00, 151.00) <0.001 176.52 Collections, M (Q1, Q3) 22.00 (4.00, 109.25) 41.00 (13.00, 257.00) 3.00 (1.00, 40.00) 21.00 (6.00, 77.00) <0.001 75.34 Comments, M (Q1, Q3) 3.00 (0.00, 19.00) 10.00 (3.00, 36.00) 0.00 (0.00, 2.00) 2.00 (0.00, 17.00) <0.001 122.99 Shares, M (Q1, Q3) 10.00 (1.00, 52.25) 30.00 (9.00, 182.00) 1.00 (0.00, 7.50) 12.00 (2.00, 40.00) <0.001 124.29 Duration, M (Q1, Q3) 95.00 (54.75, 190.25) 84.00 (55.00, 138.00) 154.00 (71.25, 775.50) 67.00 (37.00, 127.00) <0.001 47.22 Video publisher <0.001 64.76 Medical professionals, n (%) 337 (73.9%) 178 (86.8%) 95 (66.9%) 64 (58.7%) Non-medical professionals, n (%) 62 (13.6%) 6 (2.9%) 27 (19.0%) 29 (26.6%) Medical organizations, n (%) 27 (5.9%) 3 (1.5%) 17 (12.0%) 7 (6.4%) Non-medical organizations, n (%) 30 (6.6%) 18 (8.8%) 3 (2.1%) 9 (8.3%) video quality mDISCERN score, M (Q1, Q3) 3.00 (3.00, 3.00) 3.00 (3.00, 4.00) 3.00 (3.00, 3.00) 3.00 (3.00, 3.00) 0.040 6.45 GQS score, M (Q1, Q3) 3.00 (3.00, 4.00) 3.00 (3.00, 4.00) 3.00 (3.00, 4.00) 3.00 (2.00, 4.00) <0.001 14.44 Regarding video publishers, the proportion of medical professionals was highest on TikTok compared with Bilibili and RedNote (86.8% vs 66.9% vs 58.7%; P < 0.001). Meanwhile, RedNote showed a higher proportion of non-medical professionals compared with the other platforms (26.6% vs 2.9% vs 19.0%; P < 0.001). Figure 2 depicts the variation in publisher composition across the three platforms. Regarding video quality assessment, significant differences in GQS score distributions were observed among platforms ( P < 0.001). Dunn’s post-hoc pairwise comparisons with Holm’s correction revealed that videos from RedNote exhibited significantly lower GQS scores compared with both Bilibili ( P = 0.035) and TikTok ( P 0.05). Notably, despite identical median scores across platforms (median = 3.00), RedNote demonstrated a wider distribution toward lower scores (first quartile: 2.00 vs. 3.00 for Bilibili and TikTok), reflecting a higher proportion of low-quality content. For mDISCERN scores, although the overall test was significant ( P = 0.040), no pairwise comparisons remained significant after Holm’s adjustment. Figure 3 illustrates these distributional patterns across platforms. 3.2 Comparison of video characteristics among publisher types Analysis of Table 2 revealed significant differences in dissemination-related metrics among publisher types. Videos published by non-medical organizations showed significantly higher engagement than those produced by other publisher groups, as reflected by greater numbers of likes, comments, and shares ( P < 0.001 for likes and shares; P = 0.004 for comments). Conversely, no significant difference was found in the number of collections among different publisher types ( P = 0.140). In addition, videos uploaded by medical organizations had significantly longer durations compared with those from medical professionals, non-medical professionals, and non-medical organizations ( P < 0.001). Table 2 Characteristics, quality, and reliability scores of videos about pulmonary embolism by different publishers on TikTok/Bilibili/Rednote Variables Total (n = 456) Medical Professionals (n = 337) Non-medical Professionals (n = 62) Medical Organizations (n = 27) Non-medical Organizations (n = 30) P-value H Likes, M (Q1, Q3) 50.50 (10.00, 262.75) 59.00 (11.00, 270.00) 18.50 (2.00, 148.75) 21.00 (4.00, 92.50) 134.00 (45.25, 737.25) <0.001 18.72 Collections, M (Q1, Q3) 22.00 (4.00, 109.25) 23.00 (5.00, 105.00) 9.00 (1.00, 79.50) 33.00 (7.50, 121.50) 34.00 (6.00, 137.75) 0.140 5.52 Comments, M (Q1, Q3) 3.00 (0.00, 19.00) 4.00 (1.00, 19.00) 1.00 (0.00, 19.00) 0.00 (0.00, 3.50) 8.00 (1.00, 45.75) 0.004 13.21 Shares, M (Q1, Q3) 10.00 (1.00, 52.25) 10.00 (1.00, 59.00) 2.50 (0.00, 32.75) 11.00 (1.00, 52.00) 39.50 (14.50, 105.25) <0.001 18.30 Duration, M (Q1, Q3) 95.00 (54.75, 190.25) 102.00 (59.00, 178.00) 96.50 (45.25, 246.25) 231.00 (82.00, 1207.00) 14.50 (10.25, 57.75) <0.001 53.54 mDISCERN Score, M (Q1, Q3) 3.00 (3.00, 3.00) 3.00 (3.00, 3.00) 3.00 (2.00, 3.00) 3.00 (3.00, 4.00) 3.00 (3.00, 4.00) <0.001 53.63 GQS Score, M (Q1, Q3) 3.00 (3.00, 4.00) 3.00 (3.00, 4.00) 3.00 (2.00, 3.00) 4.00 (3.00, 5.00) 3.00 (2.00, 3.00) <0.001 70.90 Regarding video quality assessment, significant differences were observed in GQS scores among the four publisher types ( P < 0.001). Specifically, videos from medical organizations demonstrated significantly higher GQS scores compared to those from medical professionals, non-medical professionals, and non-medical organizations. For mDISCERN scores, significant intergroup differences were also detected ( P < 0.001), with medical organizations and non-medical organizations showing higher upper quartiles compared to medical professionals and non-medical professionals. Post-hoc analyses confirmed medical organizations’ significant GQS advantage over all comparators (P ≤ 0.005), and revealed non-medical professionals’ significantly lower mDISCERN scores relative to medical entities (both P < 0.001). Figure 4 further illustrates the disparities in the distributions of GQS and mDISCERN scores among publisher types. 3.3 Content characteristics of videos As shown in Table 3 , the distribution of pulmonary embolism–related video content differed significantly across TikTok, Bilibili, and RedNote ( P = 0.003, based on n = 962 coded content items). Therapeutic strategies (23.6%) and diagnostic criteria (22.5%) were the most common topics overall, with RedNote showing the highest proportion of therapeutic content (29.5%) and Bilibili showing the highest proportion of diagnostic criteria content (26.3%). Table 3 Comparison of content of videos about pulmonary embolism on TikTok/ Bilibili/ Rednote Variables Total TikTok Bilibili RedNote P-value χ 2 Tpyes of video content a 0.003 30.12 Diagnostic criteria, n (%) 216 (22.5%) 86 (20.4%) 87 (26.3%) 43 (20.5%) Disease definition, n (%) 66 (6.9%) 24 (5.7%) 24 (7.3%) 18 (8.6%) Epidemiology, n (%) 23 (2.4%) 8 (1.9%) 9 (2.7%) 6 (2.9%) Pathological mechanisms, n (%) 170 (17.7%) 85 (20.2%) 50 (15.1%) 35 (16.7%) Preventive strategies, n (%) 123 (12.8%) 74 (17.6%) 27 (8.2%) 22 (10.5%) Risk factors, n (%) 137 (14.2%) 59 (14.0%) 54 (16.3%) 24 (11.4%) Therapeutic strategies, n (%) 227 (23.6%) 85 (20.2%) 80 (24.2%) 62 (29.5%) Whether VTE prophylaxis was mentioned b 0.001 12.76 Yes, n (%) 151 (33.3) 76 (37.1%) 30 (21.1%) 25 (22.9%) No, n (%) 305 (67.0) 129 (62.9%) 112 (78.9%) 84 (77.1%) Types of VTE prophylaxis c 0.065 8.82 Basic prophylaxis 113 (70.6%) 70 (78.7%) 26 (66.7%) 17 (53.1%) Mechanical prophylaxis 19 (11.9%) 6 (6.7%) 6 (15.4% 7 (21.9%) Pharmacological prophylaxis 28 (17.5%) 13 (14.6%) 7 (17.9%) 8 (25.0%) Note: Abbreviations: GQS, Global Quality Score; mDISCERN, modified DISCERN; VTE, venous thromboembolism. a Percentages were calculated based on the total number of coded content items (Total = 962; TikTok = 421; Bilibili = 331; RedNote = 210). b Percentages were calculated based on the total number of videos (Total = 456; TikTok = 205; Bilibili = 142; RedNote = 109). c Percentages were calculated based on the total number of coded VTE prophylaxis items (Total = 160; TikTok = 89; Bilibili = 39; RedNote = 32). The total number of coded items may exceed the number of videos because a single video could include multiple coded items. Regarding VTE prophylaxis mentions (based on n = 456 videos), the proportion of videos mentioning VTE prophylaxis also differed significantly among platforms ( P = 0.001). Overall, only 33.3% of videos mentioned VTE prophylaxis, with the highest rate on TikTok (37.1%) and lower rates on Bilibili (21.1%) and RedNote (22.9%). Among the 160 coded prophylaxis items, the distribution of prophylaxis types did not differ significantly across platforms ( P = 0.065), although basic prophylaxis constituted the predominant category overall (70.6%). Differences in content distribution across platforms are illustrated in Fig. 5 . 3.4 Correlation analysis Spearman correlation analysis was performed to examine the relationships among video characteristics, engagement behaviors, and quality and reliability scores in pulmonary embolism–related videos (Fig. 6 ). The four engagement behaviors (like, collection, comment, and share counts) were strongly correlated with one another, with correlation coefficients ranging from 0.76 to 0.90 ( P < 0.001). In contrast, video duration exhibited weak or non-significant correlations with engagement behaviors and showed only small but significant associations with collections count (r = 0.14) and mDISCERN score (r = 0.22), while demonstrating a moderate correlation with GQS (r = 0.40). The mDISCERN score was moderately correlated with GQS (r = 0.50). Both quality indicators showed weak positive correlations with engagement metrics (r = 0.15–0.21). The presence of VTE-related information was not associated with engagement metrics or video duration but showed a small yet significant correlation with both mDISCERN score and GQS (r = 0.21). Overall, engagement behaviors were highly interrelated, whereas video characteristics and content quality demonstrated more moderate and differentiated associations with engagement. 3.5 Factors associated with video quality and reliability To identify factors independently associated with video quality and reliability, ordinal logistic regression analyses were conducted using GQS and mDISCERN scores as outcome variables. In the ordinal logistic regression model for GQS, publisher type emerged as a strong independent predictor of quality (Table 4 ). Compared with videos published by medical professionals, those uploaded by medical organizations were associated with a substantially higher likelihood of achieving higher GQS scores (OR = 4.69, 95% CI: 2.10–10.46; P < 0.001). In contrast, videos published by non-medical organizations (OR = 0.11, 95% CI: 0.05–0.24; P < 0.001) and non-medical professionals (OR = 0.18, 95% CI: 0.10–0.32; P < 0.001) were significantly associated with lower GQS scores. Content characteristics also influenced video quality: videos that did not mention VTE were less likely to receive higher GQS scores compared with those that mentioned VTE (OR = 0.42, 95% CI: 0.28–0.63; P < 0.001). Factors associated with video GQS scores are illustrated in Fig. 7 . Table 4 Ordinal logistic regression analysis of factors influencing the GQS scores of pulmonary embolism videos Variable OR Coefficient P-value 95% CI Lower 95% CI Upper VIF Video interaction index 1.03 0.031 0.506 0.94 1.13 1.026 Platform RedNote 0.72 -0.333 0.192 0.43 1.18 1.378 Platform TikTok 1.22 0.198 0.377 0.79 1.89 1.537 VTE mentioned(Y/N) VTE not mentioned 0.42 -0.858 0.000 0.28 0.63 1.033 Publisher medical organizations 4.69 1.545 0.000 2.10 10.46 1.078 Publisher non-medical rganizations 0.11 -2.206 0.000 0.05 0.24 1.029 Publisher non-medical professionals 0.18 -1.736 0.000 0.10 0.32 1.132 Note: VTE, venous thromboembolism. Similarly, in the ordinal logistic regression model for mDISCERN, publisher type remained an independent predictor of video reliability (Table 5 ). Compared with videos published by medical professionals, those uploaded by medical organizations were associated with a higher likelihood of achieving higher mDISCERN scores (OR = 2.55, 95% CI: 1.10–5.92; P = 0.029). In contrast, videos published by non-medical professionals were significantly less likely to achieve higher mDISCERN scores (OR = 0.07, 95% CI: 0.04–0.15; P < 0.001), whereas no significant association was observed for videos published by non-medical organizations. Videos that did not mention VTE were also significantly less likely to achieve higher mDISCERN scores compared with those that mentioned VTE (OR = 0.42, 95% CI: 0.27–0.66; P < 0.001). Factors associated with video GQS scores are illustrated in Fig. 8 . Table 5 Ordinal logistic regression analysis of factors influencing the mDISCERN scores of pulmonary embolism videos Variables OR Coefficient P-value 95% CI Lower 95% CI Upper VIF Video interaction index 0.98 -0.021 0.751 0.86 1.12 1.026 Platform RedNote 1.07 0.069 0.804 0.62 1.85 1.378 Platform TikTok 1.16 0.146 0.574 0.70 1.92 1.537 VTE mentioned (Y/N) VTE not mentioned 0.42 -0.873 0.000 0.27 0.66 1.033 Publisher medical organizations 2.55 0.937 0.029 1.10 5.92 1.078 Publisher non-medical organizations 0.77 -0.267 0.574 0.30 1.94 1.029 Publisher non-medical professionals 0.07 -2.605 0.000 0.04 0.15 1.132 Note: VTE, venous thromboembolism. 4 Discussion Pulmonary embolism (PE) remains a major cause of preventable morbidity and mortality worldwide, yet its nonspecific clinical presentation contributes to delayed recognition and persistently low public awareness compared with myocardial infarction and stroke [ 24 ]. Although video-based health education has shown promise in enhancing early symptom recognition and reducing diagnostic delays, systematic evaluations of PE-specific video content on social media platforms remain scarce [ 25 ]. Previous studies have primarily focused on text-based materials or have assessed venous thromboembolism as a broad category [ 19 , 25 ], leaving uncertainty regarding the quality and reliability of PE-focused video content. To our knowledge, this study provides one of the first systematic cross-platform analyses of PE-related videos on major Chinese platforms (TikTok, Bilibili, and RedNote), evaluating both informational quality and reliability. A key finding of this study is the substantial heterogeneity in the ecosystem of PE-related health information across platforms. TikTok exhibited higher professional participation and higher user interaction, with medical professionals accounting for 86.8% of content publishers. Furthermore, it also demonstrated the highest numbers of likes, comments, shares, and collections, followed by RedNote, whereas Bilibili showed the lowest engagement. Similar platform-specific disparities have been reported in previous studies [ 26 , 27 ]. TikTok emphasizes short, fast-paced content, which facilitates rapid browsing and immediate user interaction [ 28 ]. In contrast, Bilibili features significantly longer video durations and the highest proportion of diagnostic criteria-related content (26.3%), reflecting the platform’s community culture that favors in-depth, systematic knowledge acquisition [ 29 , 30 ]. Notably, RedNote, a lifestyle-oriented social media platform, had the lowest proportion of medical professionals (58.7%) and the highest proportion of non-medical professionals (26.6%) [ 31 ]. Although median GQS scores were comparable across platforms, RedNote showed a lower first quartile, suggesting a higher proportion of low-quality videos. This finding indicates that lifestyle-oriented platforms may constitute an underexplored context for health information dissemination, suggesting that enhanced regulatory vigilance may be warranted [ 32 ]. Spearman correlation analysis showed that GQS and mDISCERN scores were only weakly correlated with engagement metrics such as likes and collections. This apparent misalignment suggests that engagement-based metrics may not serve as reliable indicators of informational quality for PE-related content. Recent evidence indicates that the spread of health-related content is strongly shaped by the interaction between human attention biases and algorithmic recommendation systems, which often favor emotionally salient and highly engaging material over evidence-based information [ 33 ]. Health misinformation has increasingly been recognized as a major public health challenge, and algorithm-driven amplification may contribute to its diffusion across platforms [ 34 , 35 ]. In addition, clickbait-oriented presentation strategies may further enhance visibility and engagement regardless of informational quality [ 36 ]. Meanwhile, systematic reviews have highlighted that digital health literacy remains insufficient in many populations, limiting users’ ability to evaluate the reliability of online health information and increasing vulnerability to misleading content [ 37 , 38 ]. In the context of information overload and the ongoing “infodemic”, these factors may make it difficult for high-quality content to gain visibility. Therefore, evaluations of medical science communication may benefit from incorporating content quality and scientific rigor, rather than relying solely on engagement-based indicators. Our ordered logistic regression analysis identified publisher type as an independent predictor of video quality. Content produced by medical organizations was consistently associated with higher informational quality and reliability compared with individual or non-medical sources. This disparity may reflect institutional advantages in standardized review procedures, reputational accountability, and greater resource investment—features commonly emphasized in frameworks for trustworthy health communication [ 39 ]. Moreover, source credibility research suggests that perceived expertise plays a central role in shaping audience evaluations of information quality [ 40 ], which may partly explain the higher quality scores observed in institution-produced videos. In contrast, videos originating from non-medical entities were less likely to meet higher quality thresholds, underscoring the importance of professional verification mechanisms in digital health ecosystems. Taken together, these findings suggest the potential value of clearer credential disclosure and professional verification mechanisms on social media platforms [ 39 ], and underscore the need for greater institutional engagement in improving publicly accessible health information. Regarding content characteristics, significant heterogeneity was observed in the thematic distribution of PE-related videos across platforms ( P = 0.003), with a predominant focus on topics such as treatment strategies and pathophysiological mechanisms. Foundational topics, including epidemiology and disease definition, were comparatively underrepresented, suggesting a fragmented presentation of core knowledge elements. Notably, only 33.3% of videos mentioned preventive strategies for VTE, indicating that prevention-related information remains underrepresented in online health education, which aligns with findings from other disease-specific video analyses reporting insufficient coverage of prevention and long-term management topics [ 41 ]. Because pulmonary embolism represents a major clinical manifestation of VTE and most events originate from deep vein thrombosis [ 42 ], prevention messaging was evaluated within the framework of VTE prophylaxis in accordance with current clinical guidelines [ 43 ]. Among the videos that addressed prevention, basic measures predominated (70.6%), whereas pharmacological and mechanical prophylaxis were rarely discussed, suggesting that preventive information remains limited and incomplete. Given the high mortality yet preventable nature of PE [ 44 ], the limited emphasis on guideline-oriented prophylaxis may represent a missed opportunity for proactive risk communication, particularly for high-risk populations such as perioperative or immobilized patients. Overall, PE-related content on social media appears fragmented, with limited coverage of foundational concepts and guideline-recommended prevention strategies. These findings suggest that more comprehensive, evidence-based, and structured online health education may be beneficial to address these gaps. This study provides cross-platform evidence that engagement-based indicators may not serve as reliable proxies for informational quality in PE-related videos. The strong influence of publisher type further suggests that institutional participation may play an important role in ensuring credible online health communication. Additionally, the limited coverage of VTE prophylaxis highlights an important educational gap that may undermine prevention awareness among high-risk populations. These findings suggest that more comprehensive, evidence-based, and structured online health education may be beneficial in addressing these gaps. 5 Limitations This study has several limitations. First, the cross-sectional design captured video characteristics and engagement metrics at a single time point, which may not reflect temporal changes. Second, although GQS and mDISCERN are widely used instruments, some degree of subjectivity in scoring is unavoidable. Third, only three major Chinese video-sharing platforms were included, and international platforms such as YouTube were not assessed, which may limit the generalizability of our findings. Fourth, although a standardized medical term was used to enhance reproducibility, videos using alternative lay expressions may not have been fully captured. Finally, viewer-level outcomes such as knowledge acquisition or behavioral change were not evaluated. 6 Conclusion In conclusion, pulmonary embolism–related videos on TikTok, Bilibili, and RedNote showed substantial heterogeneity in engagement patterns, content coverage, and information quality. Overall, videos produced by medical organizations demonstrated significantly higher quality and reliability than those published by non-medical sources. Moreover, videos explicitly mentioning VTE-related information were more likely to achieve higher GQS and mDISCERN scores. These findings highlight potential opportunities to improve the completeness and reliability of pulmonary embolism health information on video-sharing platforms and suggest that greater institutional involvement could help enhance the quality of publicly available educational content. Declarations Acknowledgements: Not applicable. Funding This work was supported by Drug Safety Scientific Research Project of Guangxi Zhuang Autonomous Region Medical Products Administration (Guiyao Jianke Zhishu [2024] No. 018). Ethics approval and consent to participate: This study was based solely on publicly available data obtained from online platforms. All data were collected and analyzed in an anonymized manner, and no human participants were directly recruited or involved. Therefore, approval from an institutional review board was not required. Consent for publication: Not applicable. Availability of data and material The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. All data were collected from publicly accessible online platforms, including TikTok, Bilibili, and Rednote. Author contributions N.L. and L.D. performed the data analysis and prepared the manuscript. N.L. and J.W. collected the data. G.J. and J.H. designed the study and provided critical revisions of the manuscript. All authors contributed to the interpretation of the results, reviewed and revised the manuscript, and approved the final version. N.L. and L.D. contributed equally to this work. Competing interests The authors declare no competing interests. References Barco, S. et al. Global reporting of pulmonary embolism–related deaths in the world health organization mortality database: vital registration data from 123 countries. Res Pract. Thromb. Haemost 5 , (2021). Freund, Y., Cohen-Aubart, F. & Bloom, B. Acute pulmonary embolism: a review. JAMA 328 , 1336–1345 (2022). Glazier, C. R. & Jr, F. A. B. Epidemiology, etiology, and pathophysiology of pulmonary embolism. Int. J. Angiol. 33 , 076–081 (2024). Hagiya, H. et al. 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Quality and content analysis of syphilis-related short videos on douyin (TikTok): a cross-sectional study. Digit. HEALTH . 12 , 20552076261416314 (2026). Merrigan, J. M., Piazza, G., Lynm, C. & Livingston, E. H. Pulmonary embolism. JAMA 309 , 504 (2013). Pulmonary Embolism and Pulmonary Vascular Diseases Group of the Chinese Thoracic Society, Pulmonary Embolism and Pulmonary Vascular Disease Working Committee of the Chinese Association of Chest Physicians, & National Cooperation Group on Prevention and Treatment of Pulmonary Embolism and Pulmonary Vascular Diseases. Chinese guidelines for the diagnosis, treatment, prophylaxis and management of pulmonary thromboembolism (2025 edition). Natl. Med. J. China . 105 , 2162–2194 (2025). Zhen, K. et al. Epidemiology of pulmonary embolism in China, 2021: a nationwide hospital-based study. Lancet Reg. Health – West. Pac 54 , (2025). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 10 May, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviews received at journal 25 Mar, 2026 Reviewers agreed at journal 22 Mar, 2026 Reviewers invited by journal 13 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Editor invited by journal 16 Feb, 2026 Submission checks completed at journal 15 Feb, 2026 First submitted to journal 15 Feb, 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-8864402","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":605821523,"identity":"56e077a6-4b1b-45f7-b740-27d92de9c48d","order_by":0,"name":"Ning Li","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Li","suffix":""},{"id":605821532,"identity":"c8b8d610-30e5-4dc6-b85b-0bb2276fcbeb","order_by":1,"name":"Lilin Deng","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lilin","middleName":"","lastName":"Deng","suffix":""},{"id":605821534,"identity":"cab019a7-6c3b-4942-9d9c-8bb52b9f2024","order_by":2,"name":"Jinxia Wu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinxia","middleName":"","lastName":"Wu","suffix":""},{"id":605821538,"identity":"8b902d3a-35cc-4615-aa25-ccaec52aff3f","order_by":3,"name":"Junhe Huang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junhe","middleName":"","lastName":"Huang","suffix":""},{"id":605821553,"identity":"d2ea93f9-338e-4c04-b22a-4c44bea497fa","order_by":4,"name":"Guojun Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIie3RMQrCMBSA4ReE2CHQTSqCvUKki0OhV4mLk4gguDoImdq9xUvUG6Q8cNMLuBS8QMYKDqaTm4mbYP4ly/t4JAHw+X6w7KCIMueeDg9K6c6BcCqgJyJk50VT5d+QcblKMKBOhHH1kLjmaqURGMThSH0mmSFNIZdbri41buYwq47CsiXOORKZkropaiwZCH6zEbPFkIjUyFpk1J2ki0oycCV00+TXZRIyys0jR/a7ZHJwarsdTml8v2vdpXE4sRCAgAN5f0dkG+8btgBPl0Gfz+f7217gw0mkWtu2XwAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Guilin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Guojun","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2026-02-12 16:54:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8864402/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8864402/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104753024,"identity":"daf10321-300c-45e0-8ee9-d31f548d11f1","added_by":"auto","created_at":"2026-03-16 20:43:59","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":428734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSearch strategy and video screening procedure.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8864402/v1/9cbfac5b2df3ac489828241e.jpeg"},{"id":104783000,"identity":"2e4848ea-f2fe-47fb-a95e-e33d83dbf9b4","added_by":"auto","created_at":"2026-03-17 07:58:05","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":180537,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of video publishers on different platforms. \u003c/strong\u003e(A) Overall distribution of publisher types among all included videos. (B) Distribution of publisher types stratified by platfor\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8864402/v1/bc6a6303bef44318f2a0ec13.jpeg"},{"id":104782992,"identity":"f47103a5-8a71-4bea-8645-cf17fdc2ed36","added_by":"auto","created_at":"2026-03-17 07:58:04","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":151190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of the scores of GQS and mDISCERN across different platforms. \u003c/strong\u003e(A) Distribution of GQS scores and (B) mDISCERN scores for videos on TikTok, Bilibili, and Rednote. Violin plots represent the distribution density of scores, with embedded boxplots indicating the median and interquartile range (IQR). Differences among platforms were analyzed using the Kruskal-Wallis test, followed by Dunn’s post hoc pairwise comparisons with Holm correction. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. Abbreviations: GQS, Global Quality Score; mDISCERN, modified DISCERN.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8864402/v1/02b12644dfc2ebf1f0026cdc.jpeg"},{"id":104753022,"identity":"f8da7fb1-3a0a-49dc-8f9b-40c52802594b","added_by":"auto","created_at":"2026-03-16 20:43:58","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of the scores of GQS and mDISCERN by different publishers. \u003c/strong\u003e(A) Distribution of GQS scores and (B) mDISCERN scores among videos published by medical professionals, non-medical professionals, medical organizations, and non-medical organizations. Violin plots represent the distribution density of scores, with embedded boxplots indicating the median and IQR. Differences among publisher types were analyzed using the Kruskal-Wallis test, followed by Dunn’s post hoc pairwise comparisons with Holm correction. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001. Abbreviations: GQS, Global Quality Score; mDISCERN, modified DISCERN.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8864402/v1/5ffeca8da15b713ceec2b098.jpeg"},{"id":104753025,"identity":"09899cdd-4679-496c-b47a-26fb52f68f16","added_by":"auto","created_at":"2026-03-16 20:43:59","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":428509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of pulmonary embolism-related video content across platforms.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Overall distribution of content items (n = 962). (B) Platform-specific distribution of content items. (C) Video-level prevalence of VTE prophylaxis mentions (n = 456 videos). (D) Platform-specific comparison of VTE prophylaxis mentions. (E) Distribution of prophylaxis types among videos mentioning prophylaxis (n = 160 items). (F) Platform-specific distribution of prophylaxis types. Note: Since videos could contain multiple content items, percentages in A, B, E, and F are based on content items, whereas C and D are based on videos. Abbreviations: VTE, venous thromboembolism.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8864402/v1/8007daf6e3c01db87d95ed3e.jpeg"},{"id":104753023,"identity":"31216449-afeb-4747-9c92-9308cbd149be","added_by":"auto","created_at":"2026-03-16 20:43:58","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":589264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman correlation analysis of video variables, quality and reliability scores across platform. \u003c/strong\u003e\u003cem\u003ep\u003c/em\u003e* \u0026lt; 0.05, \u003cem\u003ep\u003c/em\u003e** \u0026lt; 0.01, \u003cem\u003ep\u003c/em\u003e*** \u0026lt; 0.001, NS: non-significant. Abbreviations: GQS, Global Quality Score; mDISCERN, modified DISCERN; VTE, venous thromboembolism.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8864402/v1/4494f1ab607dc5f27fc106c6.jpeg"},{"id":104753019,"identity":"b49142a1-5c62-420e-8d24-87806893f492","added_by":"auto","created_at":"2026-03-16 20:43:58","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":248155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOrdinal logistic regression analysis of factors influencing the GQS scores of pulmonary embolism videos. \u003c/strong\u003eForest plot showing odds ratios (ORs) and 95% confidence intervals (CIs) for variables associated with GQS scores. Medical professionals, videos mentioning VTE, and Bilibili were used as reference categories. ORs greater than 1 indicate a higher likelihood of achieving higher GQS scores. Abbreviations: GQS, Global Quality Score; VTE, venous thromboembolism.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8864402/v1/3a35cf9211bc9cd1400e7419.jpeg"},{"id":104753020,"identity":"e2b9a9bb-48a2-4e47-beb1-f4f6eac168fc","added_by":"auto","created_at":"2026-03-16 20:43:58","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":242257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOrdinal logistic regression analysis of factors influencing the mDISCERN scores of pulmonary embolism videos. \u003c/strong\u003eForest plot showing odds ratios (ORs) and 95% confidence intervals (CIs) for variables associated with mDISCERN scores. Medical professionals, videos mentioning VTE, and Bilibili were used as reference categories. ORs greater than 1 indicate a higher likelihood of achieving higher mDISCERN scores. Abbreviations: mDISCERN, modified DISCERN; VTE, venous thromboembolism.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8864402/v1/b37fabf7e2db71056d62a5e3.jpeg"},{"id":104785070,"identity":"f34474e9-0adb-446a-9da6-e6cc3c75a386","added_by":"auto","created_at":"2026-03-17 08:09:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3931890,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8864402/v1/b6a703d6-8c7c-46cc-a24c-cd642fbdae1d.pdf"},{"id":104753017,"identity":"5be49453-2abf-428d-a3bf-5469e146e859","added_by":"auto","created_at":"2026-03-16 20:43:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19029,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8864402/v1/6d5c377119596cfa0e4a81f6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Popularity Does Not Reflect Quality: A Cross-Platform Assessment of Pulmonary Embolism Health Information on Chinese Video Platforms","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eVenous thromboembolism (VTE) is a complex disorder encompassing deep vein thrombosis (DVT) and its most dangerous complication, pulmonary embolism (PE) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. PE is a potentially fatal condition that occurs when a thrombus originating in the venous circulation dislodges and embolizes to the pulmonary arterial system [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Numerous risk factors have been implicated for PE, including surgery, malignancy, advanced age, and obesity, all of which are commonly encountered in modern medical settings [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. At present, PE remains a major contributor to the global disease burden, and its reported incidence appears to be increasing worldwide [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite substantial advances in diagnostic techniques and therapeutic strategies, PE continues to pose significant challenges in timely diagnosis and optimal management [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Notably, although PE is recognized as the third most frequent acute cardiovascular disease globally, it remains comparatively under-recognized when contrasted with myocardial infarction and stroke [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Consequently, improving public awareness of PE risk factors, early warning signs, and fundamental treatment principles may represent an important public health priority. Such efforts are essential for mitigating PE-related morbidity and mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, social media platforms have rapidly evolved into major channels for the dissemination of health-related information. This shift has reshaped how the public accesses and interprets medical knowledge [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Video platforms, in particular, offer visually engaging and easily accessible formats that facilitate the efficient communication of complex medical concepts to broad audiences [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. With the growing demand for accessible health education, video-based media have emerged as a promising approach for disseminating educational information related to pulmonary embolism [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn China, Douyin (TikTok), Bilibili, and Rednote are among the most influential and widely used video platforms. These platforms differ substantially in content format and audience profile [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, amid information overload and the spread of misinformation, concerns have been raised regarding the quality and reliability of health-related videos on social media [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Previous studies have evaluated health-related video content across a range of disease topics, including cancer, chronic conditions, and infectious diseases [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Nevertheless, their findings have varied considerably across platforms and disease domains.\u003c/p\u003e \u003cp\u003eDespite the increasing presence of PE\u0026ndash;related content on social media, its quality and educational value remain insufficiently evaluated. Previous studies have mainly focused on text-based patient information and have reported substantial deficiencies in the quality, usability, and readability of pulmonary embolism\u0026ndash;related materials [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In contrast, video-based content on social media has not been systematically assessed. Given the algorithm-driven nature of short-video platforms, highly disseminated health information may not necessarily be evidence-based. In this context, evaluating both the quality and reliability of PE-related video content and identifying factors associated with higher-quality information are essential for improving public health communication. Therefore, this study aimed to evaluate PE videos on TikTok, Bilibili, and Rednote with respect to content quality and reliability. The findings are expected to inform future digital health communication strategies and contribute to improved public education on pulmonary embolism.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional design to evaluate pulmonary embolism\u0026ndash;related videos on major Chinese video-sharing platforms, including TikTok (Chinese version: Douyin), Bilibili, and Rednote.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Ethical considerations\u003c/h2\u003e \u003cp\u003eAll data analyzed in this study were derived from publicly accessible videos on TikTok, Bilibili, and Rednote. Ethical approval was not required because only publicly available data were analyzed and no identifiable information was collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Search strategy and data extraction\u003c/h2\u003e \u003cp\u003eA systematic search was conducted on TikTok, Bilibili, and Rednote using the Chinese keyword \u0026ldquo;肺栓塞\u0026rdquo; (pulmonary embolism). A standardized medical term was deliberately used to ensure consistency and reproducibility of the search strategy across platforms. All searches were performed on the same day (January 9, 2026) to minimize temporal variation. To reduce potential bias arising from personalized recommendation algorithms, all user accounts were logged out prior to the searches. Browser caches and search histories were cleared, and newly registered accounts were created to ensure neutral baseline conditions. Search results were displayed using each platform\u0026rsquo;s default sorting option without applying additional filters. Videos were screened sequentially from the highest-ranked to lower-ranked results according to the platform-generated ranking. Videos were excluded if they met any of the following criteria: (1) duplicate content, (2) irrelevant content not related to pulmonary embolism, or (3) content primarily intended for advertising or commercial promotion. For duplicate videos originating from the same source, the version uploaded by a verified account was retained; if no verified account was available, the earliest uploaded video was selected. No restrictions were applied regarding publication year, video duration, or language. The detailed video selection process is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For each eligible video, the following information was extracted and recorded: platform, title, URL, video duration, numbers of likes, comments, shares, and collections, types of publishers, as well as content category.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Classification of Videos\u003c/h2\u003e \u003cp\u003eWe divided the videos into 4 groups according to the source and into 7 groups according to the content. Video sources were categorized as follows: (1) medical professionals, (2) non-medical professionals, (3) medical organizations and (4) non-medical organizations. Video content was classified into the following categories: (1) disease definition, (2) epidemiology, (3) pathological mechanisms, (4) diagnostic criteria, (5) risk factors, (6) preventive strategies, and (7) therapeutic strategies. Individual videos may encompass content spanning multiple categories. Regarding videos from preventive strategies, further classification was performed as follows: (1) basic prophylaxis, (2) mechanical prophylaxis, and (3) pharmacological prophylaxis. Data extraction was performed independently by two reviewers within a 48-hour period to minimize temporal bias. Any discrepancies were resolved through discussion, and a third reviewer was consulted when consensus could not be reached. Specific classification criteria are shown in Supplementary Material.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Video quality and reliability assessment\u003c/h2\u003e \u003cp\u003eBetween January 10 and January 12, 2026, two physicians specializing in respiratory medicine conducted quality and reliability assessments using two validated instruments: the Global Quality Score (GQS) and the modified DISCERN (mDISCERN). The GQS is a widely used tool for evaluating overall quality of health information in videos, assessing aspects such as quality, fluency, comprehensiveness, and usefulness to patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The score ranges from 1 (poor quality) to 5 (excellent overall quality). Video reliability was assessed using the mDISCERN instrument [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The mDISCERN tool consists of five dichotomous items evaluating the reliability of health information, including: (1) whether the video is clear, concise, and understandable; (2) whether valid sources are cited; (3) whether the content is presented in a balanced and unbiased manner; (4) whether additional sources are provided for further reference; and (5) whether areas of uncertainty are mentioned [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Each item was scored as \u0026ldquo;yes\u0026rdquo; (1 point) or \u0026ldquo;no\u0026rdquo; (0 points), yielding a total score ranging from 0 to 5, with higher scores indicating greater reliability. Detailed scoring criteria for both the GQS and mDISCERN instruments are provided in the Supplementary Material.\u003c/p\u003e \u003cp\u003eTo ensure rating consistency, both reviewers underwent standardized training prior to formal assessment. Videos were divided into two groups and evaluated using a cross-rating approach. When discrepancies occurred, the final score was calculated as the mean of the two ratings. Inter-rater agreement was assessed using Cohen\u0026rsquo;s κ statistic. According to the criteria proposed by Landis and Koch, κ values\u0026thinsp;\u0026gt;\u0026thinsp;0.80 indicate almost perfect agreement. In this study, inter-rater agreement was high for both GQS (κ\u0026thinsp;=\u0026thinsp;0.828) and mDISCERN (κ\u0026thinsp;=\u0026thinsp;0.869), indicating excellent consistency between reviewers.\u003c/p\u003e \u003cp\u003eTo minimize potential bias related to video popularity, reviewers were blinded to engagement-related metrics, including the number of likes, comments, shares, and collections, during the assessment process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using Python (version 3.10). Normality was assessed using the Shapiro-Wilk test. Continuous variables are presented as mean (standard deviation) or median (interquartile range), as appropriate. Group comparisons were conducted using the Student t test, Mann-Whitney U test, one-way ANOVA, or Kruskal-Wallis test. Post-hoc pairwise comparisons were performed using Dunn\u0026rsquo;s test with Holm-Bonferroni correction for multiple comparisons when Kruskal-Wallis tests indicated statistical significance. Spearman correlation analysis was used to assess associations among video characteristics, engagement metrics, and quality scores. To integrate multiple engagement metrics and reduce multicollinearity, principal component analysis (PCA) was applied, and the first principal component was extracted as the video interaction index. A two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 General characteristics of videos\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 456 videos about pulmonary embolism were included for further analysis, including 205 videos from TikTok, 142 from Bilibili, and 109 from RedNote. The numbers of likes, collections, comments, and shares differed significantly among the three platforms, with TikTok showing the highest engagement (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, video duration was significantly longer on Bilibili than on TikTok and RedNote (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics, quality, and reliability scores of videos about pulmonary embolism on TikTok/Bilibili/Rednote\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;456)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTikTok (n\u0026thinsp;=\u0026thinsp;205)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBilibili (n\u0026thinsp;=\u0026thinsp;142)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRedNote (n\u0026thinsp;=\u0026thinsp;109)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eH/χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGeneral information\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLikes, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.50 (10.00, 262.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e161.00 (61.00, 872.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.00 (1.00, 19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.00 (15.00, 151.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e176.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollections, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.00 (4.00, 109.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.00 (13.00, 257.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00 (1.00, 40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.00 (6.00, 77.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComments, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 (0.00, 19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.00 (3.00, 36.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.00 (0.00, 17.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e122.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShares, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00 (1.00, 52.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.00 (9.00, 182.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.00, 7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.00 (2.00, 40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e124.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuration, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.00 (54.75, 190.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.00 (55.00, 138.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e154.00 (71.25, 775.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67.00 (37.00, 127.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVideo publisher\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e64.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical professionals, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e337 (73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e178 (86.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95 (66.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64 (58.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-medical professionals, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical organizations, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-medical organizations, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003evideo quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emDISCERN score, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 (3.00, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.00 (3.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00 (3.00, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.00 (3.00, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGQS score, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 (3.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.00 (3.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00 (3.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.00 (2.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding video publishers, the proportion of medical professionals was highest on TikTok compared with Bilibili and RedNote (86.8% vs 66.9% vs 58.7%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Meanwhile, RedNote showed a higher proportion of non-medical professionals compared with the other platforms (26.6% vs 2.9% vs 19.0%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the variation in publisher composition across the three platforms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding video quality assessment, significant differences in GQS score distributions were observed among platforms (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Dunn\u0026rsquo;s post-hoc pairwise comparisons with Holm\u0026rsquo;s correction revealed that videos from RedNote exhibited significantly lower GQS scores compared with both Bilibili (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) and TikTok (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas no significant difference was detected between Bilibili and TikTok (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Notably, despite identical median scores across platforms (median\u0026thinsp;=\u0026thinsp;3.00), RedNote demonstrated a wider distribution toward lower scores (first quartile: 2.00 vs. 3.00 for Bilibili and TikTok), reflecting a higher proportion of low-quality content. For mDISCERN scores, although the overall test was significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040), no pairwise comparisons remained significant after Holm\u0026rsquo;s adjustment. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates these distributional patterns across platforms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of video characteristics among publisher types\u003c/h2\u003e \u003cp\u003eAnalysis of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e revealed significant differences in dissemination-related metrics among publisher types. Videos published by non-medical organizations showed significantly higher engagement than those produced by other publisher groups, as reflected by greater numbers of likes, comments, and shares (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for likes and shares; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004 for comments). Conversely, no significant difference was found in the number of collections among different publisher types (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.140). In addition, videos uploaded by medical organizations had significantly longer durations compared with those from medical professionals, non-medical professionals, and non-medical organizations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics, quality, and reliability scores of videos about pulmonary embolism by different publishers on TikTok/Bilibili/Rednote\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;456)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedical \u003c/p\u003e \u003cp\u003eProfessionals\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;337)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-medical\u003c/p\u003e \u003cp\u003e Professionals\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedical \u003c/p\u003e \u003cp\u003eOrganizations\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-medical \u003c/p\u003e \u003cp\u003eOrganizations\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikes, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.50 (10.00, 262.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.00 (11.00, 270.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.50 (2.00, 148.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.00 (4.00, 92.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e134.00 (45.25, 737.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollections, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.00 (4.00, 109.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.00 (5.00, 105.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.00 (1.00, 79.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.00 (7.50, 121.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.00 (6.00, 137.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComments, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.00 (0.00, 19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00 (1.00, 19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.00, 19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.00 (1.00, 45.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShares, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00 (1.00, 52.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00 (1.00, 59.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.50 (0.00, 32.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.00 (1.00, 52.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.50 (14.50, 105.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.00 (54.75, 190.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102.00 (59.00, 178.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.50 (45.25, 246.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e231.00 (82.00, 1207.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.50 (10.25, 57.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emDISCERN Score, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.00 (3.00, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 (3.00, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.00 (2.00, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00 (3.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.00 (3.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGQS Score, M (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.00 (3.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 (3.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.00 (2.00, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.00 (3.00, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.00 (2.00, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e70.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding video quality assessment, significant differences were observed in GQS scores among the four publisher types (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, videos from medical organizations demonstrated significantly higher GQS scores compared to those from medical professionals, non-medical professionals, and non-medical organizations. For mDISCERN scores, significant intergroup differences were also detected (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with medical organizations and non-medical organizations showing higher upper quartiles compared to medical professionals and non-medical professionals. Post-hoc analyses confirmed medical organizations\u0026rsquo; significant GQS advantage over all comparators \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.005), and revealed non-medical professionals\u0026rsquo; significantly lower mDISCERN scores relative to medical entities (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e further illustrates the disparities in the distributions of GQS and mDISCERN scores among publisher types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Content characteristics of videos\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the distribution of pulmonary embolism\u0026ndash;related video content differed significantly across TikTok, Bilibili, and RedNote (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, based on n\u0026thinsp;=\u0026thinsp;962 coded content items). Therapeutic strategies (23.6%) and diagnostic criteria (22.5%) were the most common topics overall, with RedNote showing the highest proportion of therapeutic content (29.5%) and Bilibili showing the highest proportion of diagnostic criteria content (26.3%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of content of videos about pulmonary embolism on TikTok/ Bilibili/ Rednote\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTikTok\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBilibili\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRedNote\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTpyes of video content\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiagnostic criteria, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e216 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43 (20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisease definition, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEpidemiology, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathological mechanisms, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreventive strategies, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk factors, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTherapeutic strategies, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhether VTE prophylaxis was mentioned\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305 (67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129 (62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e112 (78.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84 (77.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTypes of VTE prophylaxis\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasic prophylaxis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70 (78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17 (53.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMechanical prophylaxis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6 (15.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePharmacological prophylaxis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: Abbreviations: GQS, Global Quality Score; mDISCERN, modified DISCERN; VTE, venous thromboembolism.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e Percentages were calculated based on the total number of coded content items (Total\u0026thinsp;=\u0026thinsp;962; TikTok\u0026thinsp;=\u0026thinsp;421; Bilibili\u0026thinsp;=\u0026thinsp;331; RedNote\u0026thinsp;=\u0026thinsp;210).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e Percentages were calculated based on the total number of videos (Total\u0026thinsp;=\u0026thinsp;456; TikTok\u0026thinsp;=\u0026thinsp;205; Bilibili\u0026thinsp;=\u0026thinsp;142; RedNote\u0026thinsp;=\u0026thinsp;109).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ec\u003c/sup\u003e Percentages were calculated based on the total number of coded VTE prophylaxis items (Total\u0026thinsp;=\u0026thinsp;160; TikTok\u0026thinsp;=\u0026thinsp;89; Bilibili\u0026thinsp;=\u0026thinsp;39; RedNote\u0026thinsp;=\u0026thinsp;32).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eThe total number of coded items may exceed the number of videos because a single video could include multiple coded items.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding VTE prophylaxis mentions (based on n\u0026thinsp;=\u0026thinsp;456 videos), the proportion of videos mentioning VTE prophylaxis also differed significantly among platforms (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Overall, only 33.3% of videos mentioned VTE prophylaxis, with the highest rate on TikTok (37.1%) and lower rates on Bilibili (21.1%) and RedNote (22.9%).\u003c/p\u003e \u003cp\u003eAmong the 160 coded prophylaxis items, the distribution of prophylaxis types did not differ significantly across platforms (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.065), although basic prophylaxis constituted the predominant category overall (70.6%). Differences in content distribution across platforms are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation analysis\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis was performed to examine the relationships among video characteristics, engagement behaviors, and quality and reliability scores in pulmonary embolism\u0026ndash;related videos (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The four engagement behaviors (like, collection, comment, and share counts) were strongly correlated with one another, with correlation coefficients ranging from 0.76 to 0.90 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, video duration exhibited weak or non-significant correlations with engagement behaviors and showed only small but significant associations with collections count (r\u0026thinsp;=\u0026thinsp;0.14) and mDISCERN score (r\u0026thinsp;=\u0026thinsp;0.22), while demonstrating a moderate correlation with GQS (r\u0026thinsp;=\u0026thinsp;0.40). The mDISCERN score was moderately correlated with GQS (r\u0026thinsp;=\u0026thinsp;0.50). Both quality indicators showed weak positive correlations with engagement metrics (r\u0026thinsp;=\u0026thinsp;0.15\u0026ndash;0.21). The presence of VTE-related information was not associated with engagement metrics or video duration but showed a small yet significant correlation with both mDISCERN score and GQS (r\u0026thinsp;=\u0026thinsp;0.21). Overall, engagement behaviors were highly interrelated, whereas video characteristics and content quality demonstrated more moderate and differentiated associations with engagement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Factors associated with video quality and reliability\u003c/h2\u003e \u003cp\u003eTo identify factors independently associated with video quality and reliability, ordinal logistic regression analyses were conducted using GQS and mDISCERN scores as outcome variables.\u003c/p\u003e \u003cp\u003eIn the ordinal logistic regression model for GQS, publisher type emerged as a strong independent predictor of quality (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Compared with videos published by medical professionals, those uploaded by medical organizations were associated with a substantially higher likelihood of achieving higher GQS scores (OR\u0026thinsp;=\u0026thinsp;4.69, 95% CI: 2.10\u0026ndash;10.46; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, videos published by non-medical organizations (OR\u0026thinsp;=\u0026thinsp;0.11, 95% CI: 0.05\u0026ndash;0.24; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and non-medical professionals (OR\u0026thinsp;=\u0026thinsp;0.18, 95% CI: 0.10\u0026ndash;0.32; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with lower GQS scores. Content characteristics also influenced video quality: videos that did not mention VTE were less likely to receive higher GQS scores compared with those that mentioned VTE (OR\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.28\u0026ndash;0.63; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Factors associated with video GQS scores are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOrdinal logistic regression analysis of factors influencing the GQS scores of pulmonary embolism videos\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI Upper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVideo interaction index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform RedNote\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform TikTok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVTE mentioned(Y/N) VTE not mentioned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublisher medical organizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublisher non-medical rganizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublisher non-medical professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: VTE, venous thromboembolism.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, in the ordinal logistic regression model for mDISCERN, publisher type remained an independent predictor of video reliability (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Compared with videos published by medical professionals, those uploaded by medical organizations were associated with a higher likelihood of achieving higher mDISCERN scores (OR\u0026thinsp;=\u0026thinsp;2.55, 95% CI: 1.10\u0026ndash;5.92; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). In contrast, videos published by non-medical professionals were significantly less likely to achieve higher mDISCERN scores (OR\u0026thinsp;=\u0026thinsp;0.07, 95% CI: 0.04\u0026ndash;0.15; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas no significant association was observed for videos published by non-medical organizations. Videos that did not mention VTE were also significantly less likely to achieve higher mDISCERN scores compared with those that mentioned VTE (OR\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.27\u0026ndash;0.66; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Factors associated with video GQS scores are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOrdinal logistic regression analysis of factors influencing the mDISCERN scores of pulmonary embolism videos\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI Upper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVideo interaction index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform RedNote\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform TikTok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVTE mentioned (Y/N) VTE not mentioned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublisher medical organizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublisher non-medical organizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublisher non-medical professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: VTE, venous thromboembolism.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003ePulmonary embolism (PE) remains a major cause of preventable morbidity and mortality worldwide, yet its nonspecific clinical presentation contributes to delayed recognition and persistently low public awareness compared with myocardial infarction and stroke [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Although video-based health education has shown promise in enhancing early symptom recognition and reducing diagnostic delays, systematic evaluations of PE-specific video content on social media platforms remain scarce [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Previous studies have primarily focused on text-based materials or have assessed venous thromboembolism as a broad category [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], leaving uncertainty regarding the quality and reliability of PE-focused video content. To our knowledge, this study provides one of the first systematic cross-platform analyses of PE-related videos on major Chinese platforms (TikTok, Bilibili, and RedNote), evaluating both informational quality and reliability.\u003c/p\u003e \u003cp\u003eA key finding of this study is the substantial heterogeneity in the ecosystem of PE-related health information across platforms. TikTok exhibited higher professional participation and higher user interaction, with medical professionals accounting for 86.8% of content publishers. Furthermore, it also demonstrated the highest numbers of likes, comments, shares, and collections, followed by RedNote, whereas Bilibili showed the lowest engagement. Similar platform-specific disparities have been reported in previous studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. TikTok emphasizes short, fast-paced content, which facilitates rapid browsing and immediate user interaction [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In contrast, Bilibili features significantly longer video durations and the highest proportion of diagnostic criteria-related content (26.3%), reflecting the platform\u0026rsquo;s community culture that favors in-depth, systematic knowledge acquisition [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Notably, RedNote, a lifestyle-oriented social media platform, had the lowest proportion of medical professionals (58.7%) and the highest proportion of non-medical professionals (26.6%) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Although median GQS scores were comparable across platforms, RedNote showed a lower first quartile, suggesting a higher proportion of low-quality videos. This finding indicates that lifestyle-oriented platforms may constitute an underexplored context for health information dissemination, suggesting that enhanced regulatory vigilance may be warranted [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpearman correlation analysis showed that GQS and mDISCERN scores were only weakly correlated with engagement metrics such as likes and collections. This apparent misalignment suggests that engagement-based metrics may not serve as reliable indicators of informational quality for PE-related content. Recent evidence indicates that the spread of health-related content is strongly shaped by the interaction between human attention biases and algorithmic recommendation systems, which often favor emotionally salient and highly engaging material over evidence-based information [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Health misinformation has increasingly been recognized as a major public health challenge, and algorithm-driven amplification may contribute to its diffusion across platforms [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, clickbait-oriented presentation strategies may further enhance visibility and engagement regardless of informational quality [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Meanwhile, systematic reviews have highlighted that digital health literacy remains insufficient in many populations, limiting users\u0026rsquo; ability to evaluate the reliability of online health information and increasing vulnerability to misleading content [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In the context of information overload and the ongoing \u0026ldquo;infodemic\u0026rdquo;, these factors may make it difficult for high-quality content to gain visibility. Therefore, evaluations of medical science communication may benefit from incorporating content quality and scientific rigor, rather than relying solely on engagement-based indicators.\u003c/p\u003e \u003cp\u003eOur ordered logistic regression analysis identified publisher type as an independent predictor of video quality. Content produced by medical organizations was consistently associated with higher informational quality and reliability compared with individual or non-medical sources. This disparity may reflect institutional advantages in standardized review procedures, reputational accountability, and greater resource investment\u0026mdash;features commonly emphasized in frameworks for trustworthy health communication [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, source credibility research suggests that perceived expertise plays a central role in shaping audience evaluations of information quality [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], which may partly explain the higher quality scores observed in institution-produced videos. In contrast, videos originating from non-medical entities were less likely to meet higher quality thresholds, underscoring the importance of professional verification mechanisms in digital health ecosystems. Taken together, these findings suggest the potential value of clearer credential disclosure and professional verification mechanisms on social media platforms [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and underscore the need for greater institutional engagement in improving publicly accessible health information.\u003c/p\u003e \u003cp\u003eRegarding content characteristics, significant heterogeneity was observed in the thematic distribution of PE-related videos across platforms (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), with a predominant focus on topics such as treatment strategies and pathophysiological mechanisms. Foundational topics, including epidemiology and disease definition, were comparatively underrepresented, suggesting a fragmented presentation of core knowledge elements. Notably, only 33.3% of videos mentioned preventive strategies for VTE, indicating that prevention-related information remains underrepresented in online health education, which aligns with findings from other disease-specific video analyses reporting insufficient coverage of prevention and long-term management topics [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Because pulmonary embolism represents a major clinical manifestation of VTE and most events originate from deep vein thrombosis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], prevention messaging was evaluated within the framework of VTE prophylaxis in accordance with current clinical guidelines [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Among the videos that addressed prevention, basic measures predominated (70.6%), whereas pharmacological and mechanical prophylaxis were rarely discussed, suggesting that preventive information remains limited and incomplete. Given the high mortality yet preventable nature of PE [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], the limited emphasis on guideline-oriented prophylaxis may represent a missed opportunity for proactive risk communication, particularly for high-risk populations such as perioperative or immobilized patients. Overall, PE-related content on social media appears fragmented, with limited coverage of foundational concepts and guideline-recommended prevention strategies. These findings suggest that more comprehensive, evidence-based, and structured online health education may be beneficial to address these gaps.\u003c/p\u003e \u003cp\u003eThis study provides cross-platform evidence that engagement-based indicators may not serve as reliable proxies for informational quality in PE-related videos. The strong influence of publisher type further suggests that institutional participation may play an important role in ensuring credible online health communication. Additionally, the limited coverage of VTE prophylaxis highlights an important educational gap that may undermine prevention awareness among high-risk populations. These findings suggest that more comprehensive, evidence-based, and structured online health education may be beneficial in addressing these gaps.\u003c/p\u003e"},{"header":"5 Limitations","content":"\u003cp\u003eThis study has several limitations. First, the cross-sectional design captured video characteristics and engagement metrics at a single time point, which may not reflect temporal changes. Second, although GQS and mDISCERN are widely used instruments, some degree of subjectivity in scoring is unavoidable. Third, only three major Chinese video-sharing platforms were included, and international platforms such as YouTube were not assessed, which may limit the generalizability of our findings. Fourth, although a standardized medical term was used to enhance reproducibility, videos using alternative lay expressions may not have been fully captured. Finally, viewer-level outcomes such as knowledge acquisition or behavioral change were not evaluated.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eIn conclusion, pulmonary embolism\u0026ndash;related videos on TikTok, Bilibili, and RedNote showed substantial heterogeneity in engagement patterns, content coverage, and information quality. Overall, videos produced by medical organizations demonstrated significantly higher quality and reliability than those published by non-medical sources. Moreover, videos explicitly mentioning VTE-related information were more likely to achieve higher GQS and mDISCERN scores. These findings highlight potential opportunities to improve the completeness and reliability of pulmonary embolism health information on video-sharing platforms and suggest that greater institutional involvement could help enhance the quality of publicly available educational content.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements: Not applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Drug Safety Scientific Research Project of Guangxi Zhuang Autonomous Region Medical Products Administration (Guiyao Jianke Zhishu [2024] No. 018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based solely on publicly available data obtained from online platforms. All data were collected and analyzed in an anonymized manner, and no human participants were directly recruited or involved. Therefore, approval from an institutional review board was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication: Not applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. All data were collected from publicly accessible online platforms, including TikTok, Bilibili, and Rednote.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor \u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.L. and L.D. performed the data analysis and prepared the manuscript. N.L. and J.W. collected the data. G.J. and J.H. designed the study and provided critical revisions of the manuscript. All authors contributed to the interpretation of the results, reviewed and revised the manuscript, and approved the final version. N.L. and L.D. contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarco, S. et al. Global reporting of pulmonary embolism\u0026ndash;related deaths in the world health organization mortality database: vital registration data from 123 countries. \u003cem\u003eRes Pract. Thromb. Haemost\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreund, Y., Cohen-Aubart, F. \u0026amp; Bloom, B. 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Pac\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e, (2025).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulmonary embolism, Venous thromboembolism, Video platforms, Health information quality, mDISCERN, Global Quality Score","lastPublishedDoi":"10.21203/rs.3.rs-8864402/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8864402/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePulmonary embolism (PE)-related videos are widely consumed as online health information, yet user engagement may not reflect informational quality and reliability. We evaluated 456 PE-related videos from TikTok (n\u0026thinsp;=\u0026thinsp;205), Bilibili (n\u0026thinsp;=\u0026thinsp;142), and RedNote (n\u0026thinsp;=\u0026thinsp;109). Video characteristics, publisher type, engagement metrics, and content features were extracted. Quality and reliability were assessed using the Global Quality Score (GQS) and modified DISCERN (mDISCERN). Significant platform differences were observed in engagement patterns, publisher composition, content distribution, and quality scores. Only 33.3% of videos mentioned venous thromboembolism (VTE) prophylaxis. Engagement metrics were strongly intercorrelated but showed weak associations with GQS and mDISCERN. Ordinal logistic regression identified publisher type as a strong independent predictor of quality. Videos produced by medical organizations were more likely to achieve higher GQS (OR\u0026thinsp;=\u0026thinsp;4.69, 95% CI 2.10\u0026ndash;10.46) and mDISCERN scores (OR\u0026thinsp;=\u0026thinsp;2.55, 95% CI 1.10\u0026ndash;5.92), whereas videos not mentioning VTE were less likely to receive high scores. These findings indicate substantial heterogeneity in PE-related videos. They also suggest that highly disseminated content may not always be evidence-based, highlighting the importance of greater institutional participation and guideline-oriented prevention messaging.\u003c/p\u003e","manuscriptTitle":"Popularity Does Not Reflect Quality: A Cross-Platform Assessment of Pulmonary Embolism Health Information on Chinese Video Platforms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 20:43:53","doi":"10.21203/rs.3.rs-8864402/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T05:53:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T14:12:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114244294207312756685287673860379829830","date":"2026-04-28T15:39:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T07:04:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37456381590518760961927075436611491909","date":"2026-03-22T04:29:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-13T17:20:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-13T17:04:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-16T16:21:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T02:23:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-16T02:18:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"578af17e-8976-4289-a50a-347298fb117d","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T05:53:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T14:12:44+00:00","index":73,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":64478906,"name":"Health sciences/Diseases"},{"id":64478907,"name":"Health sciences/Health care"},{"id":64478908,"name":"Health sciences/Medical research"},{"id":64478909,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-11T06:13:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 20:43:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8864402","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8864402","identity":"rs-8864402","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}