Quality, Reliability, and Patient Educational Utility of Anesthesia-Related Videos Across Global Social Media Platforms: A Cross-Sectional Study

Quality, Reliability, and Patient Educational Utility of Anesthesia-Related Videos Across Global Social Media Platforms: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quality, Reliability, and Patient Educational Utility of Anesthesia-Related Videos Across Global Social Media Platforms: A Cross-Sectional Study Jianwen Cai, Gang Wang, Tianqi Zhang, Tao Zhu, Peiyi Li, Xuechao Hao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9635247/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: As patients increasingly rely on social media for health information, the quality, reliability, and patient-oriented utility of medical video content across digital platforms remain insufficiently understood. Objective: To evaluate and compare the quality, reliability, understandability, and actionability of anesthesia-related educational videos across major social media platforms and to identify factors associated with higher informational quality and reliability. Methods: This cross-sectional study analyzed anesthesia-related videos collected from 4 global social media platforms (Bilibili, Douyin, TikTok, and YouTube). Using bilingual keyword searches, the top-ranked 100 videos from each platform were retrieved. Videos were independently evaluated using standardized assessment tools, and primary outcomes included overall quality (Global Quality Score [GQS]), reliability (modified DISCERN [mDISCERN]), understandability and actionability (Patient Education Materials Assessment Tool [PEMAT]). Multivariable regression analyses were conducted to identify predictors of quality and reliability. Results: Among 400 videos analyzed, overall quality and reliability were moderate, with significant differences across platforms (P≤.002). YouTube videos demonstrated higher reliability and quality scores compared with other platforms, whereas TikTok videos showed lower quality despite higher user engagement. International platforms exhibited higher actionability (median PEMAT-A, 100%) compared with Chinese platforms (Douyin: 0%; Bilibili: 25%; P<.001). Longer video duration and higher understandability were positively associated with both quality and reliability. In multivariable models, understandability was the strongest predictor of overall quality (β=0.474), while video duration was the strongest predictor of reliability (β=0.434). Conclusions: In this cross-sectional analysis, anesthesia-related educational videos on social media demonstrated moderate overall quality with substantial variation in patient-oriented utility across platforms. Higher understandability and longer duration were associated with improved quality and reliability. These findings highlight opportunities for improving digital patient education through clearer communication and more actionable content. Anesthesia Social Media Patient Education Health Informatics Short Video Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction According to estimates from the Lancet, approximately 313 million surgical procedures are undertaken globally each year, suggesting that a substantial proportion of the world’s population experiences perioperative care annually.[ 1 ] Preoperative anxiety represents a pervasive challenge in global perioperative medicine, with reported prevalence rates ranging from 60% to 80% among elective surgical patients.[ 2 – 4 ] This psychological stress stems from fear of the unknown, concerns regarding pain, and catastrophic ideation such as failure to awaken or intraoperative awareness.[ 5 ] Clinical evidence indicates that severe preoperative anxiety is not merely a negative emotional experience but a catalyst for significant pathophysiological alterations[ 6 – 8 ] Furthermore, elevated anxiety levels are significantly correlated with delayed postoperative recovery, impaired wound healing, and diminished patient satisfaction.[ 9 , 10 ] Consequently, the effective identification and mitigation of preoperative anxiety are paramount for ensuring perioperative safety and improving clinical outcomes. In the traditional medical model, the mitigation of preoperative anxiety relies predominantly on preoperative visits by anesthesiologists and printed educational materials.[ 11 ] Although face-to-face communication is considered the gold standard, this intervention faces severe challenges in increasingly busy clinical practices. Studies have shown that due to time constraints in outpatient settings and the scarcity of medical resources, physicians are often unable to fully explain complex anesthetic mechanisms and risks within limited timeframes.[ 12 ] Simultaneously, constrained by health literacy levels, patient retention of verbal information is remarkably low; approximately 40%–80% of medical information is forgotten or misunderstood immediately after the consultation concludes.[ 13 ] While printed leaflets serve as supplements, they frequently lack readability due to obscure professional terminology, failing to effectively reach the patient's cognitive core.[ 14 ] This asymmetry between supply and demand necessitates more efficient forms of education to bridge the information gap. With the ubiquity of the mobile internet, a paradigm shift has occurred in how the public acquires health information. An increasing number of patients are turning to social media platforms for medical advice, where short-form videos, characterized by their vividness, fragmentation, and accessibility, have rapidly become a primary information source for the global public.[ 15 – 18 ] Although these platforms offer opportunities to fill the information vacuum, the scientific validity of their content remains largely unregulated. On one hand, social media algorithms typically prioritize content with high interaction metrics over high-quality evidence-based medical information;[ 19 ] on the other hand, a proliferation of low-quality content generated by non-professionals often contains misinformation, such as claims that general anesthesia damages the brain or that neuraxial anesthesia leads to paralysis.[ 20 ] This ‘infodemic’ fails to alleviate anxiety and may instead exacerbate patient panic,[ 21 ] potentially leading patients to refuse necessary analgesia or anesthesia techniques due to misconceptions, thereby seriously interfering with standard clinical decision-making.[ 22 , 23 ] Although previous studies have utilized tools such as DISCERN and the Global Quality Score (GQS) to evaluate online health information quality in fields like orthopedics, dermatology, and oncology,[ 24 , 25 ] there is a lack of systematic quality assessment specific to anesthesiology, a highly specialized field involving complex pharmacological mechanisms. In particular, current literature is mostly limited to single platforms or single linguistic environments, with few studies simultaneously including mainstream video platforms from different cultural backgrounds to comparatively analyze the quality differences and dissemination characteristics of anesthesia education information in the global digital ecosystem.[ 26 ] In light of this, this study aims to employ a multidimensional assessment system to comprehensively evaluate the quality, reliability, and utility of anesthesia-related short videos on four globally representative video platforms (Bilibili, Douyin, TikTok, and YouTube). Our objectives are not only to facilitate more targeted engagement with perioperative online information among both patients and anesthesiologists, but also to generate evidence-based insights to support global healthcare professionals in optimizing digital health education strategies. Methods Ethical Considerations All data for this study were obtained from publicly accessible videos on Bilibili, Douyin, TikTok, and YouTube. The research did not involve any clinical records, human biological samples, or animal experimental data. As the study relied exclusively on public, anonymized data and involved no direct interaction or intervention with platform users, it was deemed exempt from institutional review board (IRB) approval in accordance with international ethical standards and institutional regulations. Consequently, informed consent was not required. Platforms and Search Strategy This cross-sectional study included four major global video-sharing platforms: Bilibili, Douyin, TikTok, and YouTube. Data collection was conducted from November 10 to November 15, 2025. To mitigate potential bias introduced by personalized recommendation algorithms, all searches were performed using the incognito or private mode of web browsers. Prior to each search session, browser caches and history were cleared, and no personal accounts were logged in. A bilingual keyword strategy was employed to ensure comprehensive coverage of both Chinese and international platforms. Search term combinations included: ‘麻醉’ (Anesthesia), ‘全麻’ (General Anesthesia), ‘麻醉科普’ (Anesthesia Science Popularization), and ‘麻醉医生’ (Anesthesiologist) in Chinese platforms (Bilibili, Douyin); ‘anesthesia’, ‘anesthesiologist’, ‘general anesthesia’, and ‘what is anesthesia’ in English platforms (YouTube, TikTok). For each keyword on every platform, the top videos ranked by the platform's default sorting algorithm (YouTube: ‘Relevance’; TikTok: ‘Top’; Bilibili and Douyin: ‘Comprehensive’) were extracted. Inclusion criteria were: (1) primary content related to anesthesia education targeted at the general public and medical professionals; (2) language in Mandarin (Bilibili/Douyin) or English (YouTube/TikTok); and (3) video format (excluding static image slides). Exclusion criteria included: (1) duplicate videos (within platforms); (2) irrelevant content (e.g., surgical footage, music videos, news reports); (3) News, Ads, or promotions; and (4) extremely poor audio or visual quality precluding assessment. Video screening based on the predefined inclusion and exclusion criteria was independently performed by one researcher. Finally, 100 videos were selected based on the sorting principles of each platform (Fig. 1 A). Data Extraction and Classification Key metadata for each video in the final dataset were systematically recorded, including uploader source, duration, and user interaction metrics (likes, comments, and total followers). The dataset was organized and stored using Microsoft Excel (Multimedia Appendix 1). To facilitate systematic analysis, a video classification scheme was implemented. Videos were categorized into four thematic groups based on their primary subject matter: (1) The Anesthesia Profession (the role, responsibilities, working environment, and professional identity of anesthesiologists); (2) Safety, Risks, and Myth Busting (addressing anesthesia-related safety issues, potential risks or complications, and myth-busting information intended to clarify misconceptions and answer common public concerns); (3) Perioperative Process and Patient Experience (perioperative journey from the patient’s perspective, including preparation, procedural experiences, and postoperative recovery); and (4) Basics and Principles of Anesthesia (educational content explaining fundamental concepts, mechanisms, and basic medical principles of anesthesia). Furthermore, information sources were classified into five types: (1) Certified Medical Professionals (CMP); (2) Official Health Institutions (OHI); (3) Science Communicators (SC); (4) Patients and Caregivers (PC); and (5) Other/Non-Professional Media (accounts not fitting the above categories, such as entertainment channels). The two researchers jointly completed the data extraction and video topic classification. In cases of disagreement between the two researchers, a third reviewer (PL) served as an arbitrator and assigned the final classification (Fig. 1 B). Quality and Utility Assessments All videos were collected and downloaded by a single researcher and were independently assessed and scored by two trained raters across all platforms using the predefined scoring instruments, including Global Quality Score (GQS), modified DISCERN (mDISCERN), and Patient Education Materials Assessment Tool (PEMAT), the details of these instruments were followed: Global Quality Score (GQS) [ 27 ] A 5-point scale used to evaluate the overall informational quality and flow of the video (1 = poor quality, content completely useless; 5 = excellent quality, rigorous logic, comprehensive content, and highly useful). Modified DISCERN (mDISCERN) [ 28 ] A 5-point cumulative scale (0–5) used to assess content reliability based on five binary (Yes = 1/No = 0) questions: (1) Is the video's purpose clear? (2) Are reliable information sources cited? (3) Is the content balanced and unbiased? (4) Are additional information sources provided for patient reference? (5) Are areas of uncertainty mentioned? Patient Education Materials Assessment Tool (PEMAT) [ 29 ] A core instrument for evaluating health education materials from the patient's perspective. It comprises two domains: Understandability (PEMAT-U): Calculated based on 13 items (e.g., language, organization, layout, visual aids) to measure how easily the audience can understand the material (score: 0–100%). Actionability (PEMAT-A): Calculated based on 4 items (e.g., clear action instructions, provision of tools) to measure the material's effectiveness in empowering patients to take action (score: 0–100%). Prior to formal independent scoring, both raters used the same predefined scoring criteria and independently evaluated ten videos from each platform that were not included in the final analysis to calibrate scoring interpretation and ensure cross-platform consistency. Interrater and intrarater reliability were assessed using the intraclass correlation coefficient (ICC).[ 30 ] The results demonstrated good agreement, with ICC values exceeding 0.70 across platforms and scoring rounds, indicating acceptable rating consistency. Following independent scoring, discrepancies were resolved through structured discussion based on predefined scoring criteria and video content. When disagreement persisted, a third rater served as an arbitrator and assigned the final scores. Final ratings were confirmed by consensus among all authors (Fig. 1 C). Statistical Analysis Data normality was assessed using the Shapiro-Wilk test, which indicated non-parametric distributions. Consequently, key variables were summarized descriptively using medians and interquartile ranges (IQRs). Inter-group differences were evaluated using the Mann-Whitney U test, with Dunn’s test employed for pairwise post-hoc comparisons. Inter-rater reliability was assessed by calculating Cohen’s kappa coefficient. Spearman and Pearson correlation analyses were conducted to explore relationships between variables. Stepwise multivariable linear regression were developed to evaluate the impact of video variables on predicting video quality and reliability. A P-value of < .05 was considered statistically significant. All data analyses and model construction were performed using Python (version 3.12.7). Results Video Characteristics The general characteristics of the analyzed videos are summarized in Table 1. The results indicated statistically significant differences across platforms in terms of user engagement metrics as well as assessed content quality, reliability, and utility scores (all P <.002). Consequently, Dunn’s test was utilized for pairwise post-hoc comparisons to delineate specific inter-platform differences. To control for the increased risk of Type I error associated with multiple comparisons, adjusted P-values were calculated. Detailed results of these post-hoc comparisons are provided in Multimedia Appendix 2. As detailed in Multimedia Appendix 3 and Multimedia Appendix 4, the distribution of video sources and content themes varied significantly. Table 1. Characteristics and evaluation scores of anesthesia-related videos across platforms. Variable Bilibili Douyin TikTok YouTube Kruskal-Wallis H test (df) Asymptotic significance Duration (s) 180 (98–354) 71 (55–123) 59 (42–87) 86 (32–380) 65.743 (3) < 0.001 Days since upload (d) 766 (320–1320) 222 (67–610) 564 (282–973) 1014 (455–1936) 66.704 (3) < 0.001 Likes 182 (32–2688) 983 (109–4192) 4442 (804–27950) 2225 (103–10675) 38.649 (3) < 0.001 Comments 18 (2–282) 122 (13–711) 136 (28–535) 69 (3–482) 18.423 (3) < 0.001 Followers 5034 (406–110250) 26000 (5326–104250) 1598 (797–4636) 186000 (2190–588500) 56.379 (3) < 0.001 GQS 3 (3–4) 3 (3–4) 3 (2–3) 3 (3–4) 15.181 (3) 0.002 mDISCERN 3 (2–3) 3 (2–3) 3 (2–3) 3 (3–4) 24.097 (3) < 0.001 PEMAT-U 58.3 (50.0–72.7) 72.7 (63.6–77.8) 69.2 (58.3–76.9) 75.0 (63.6–84.6) 41.959 (3) < 0.001 PEMAT-A 25.0 (0.0–66.7) 0.0 (0.0–50.0) 100.0 (100.0–100.0) 100.0 (66.7–100.0) 178.001 (3) < 0.001 Note: Values are presented as median (IQR) unless otherwise indicated. Abbreviations: GQS, Global Quality Score; mDISCERN, modified DISCERN; PEMAT-U, Patient Education Materials Assessment Tool–Understandability; PEMAT-A, Patient Education Materials Assessment Tool–Actionability; IQR, Interquartile Range. CMP were the predominant source type (222/400, 55.5%), with the highest proportion observed on TikTok (80%), followed by Douyin (58%) and YouTube (52%). In contrast, Bilibili showed a lower proportion of CMP-generated videos (32%) and relatively higher representation of SCs (28%) and Other sources (27%). Videos produced by PC were rare across all platforms (0%–3%). Regarding content themes, “Basics and Principles of Anesthesia” was the most prevalent category, accounting for approximately half or more of videos across platforms, including TikTok (76%) and YouTube (75%). “The Anesthesia Profession” also appeared frequently, particularly on Douyin (22%). Post-hoc analyses revealed substantial disparities in video characteristics across platforms. Specifically, Bilibili videos exhibited a significantly longer median duration (180 seconds, IQR 98–354), far exceeding content on TikTok (59 seconds, P<.001) and Douyin (71 seconds, P<.001). In terms of user engagement, TikTok demonstrated exceptionally high interactivity, with a median like count of 4,442 (IQR 804–27,950), which was significantly higher than that of Bilibili (182, P<.001) and Douyin (983, P<.001). However, regarding utility (PEMAT-A), YouTube and TikTok stood out with high scores, both achieving a median of 100% (IQR 66.7%–100%), whereas Chinese platforms Douyin (0%) and Bilibili (25%) scored significantly lower (both P<.001). This suggests that although Bilibili provides more detailed (longer duration) information, international platforms outperform them in translating information into actionable patient guidance. Quality and Reliability Assessment Inter-platform comparisons demonstrated distinct quality profiles (Figure 2A). Videos on YouTube and Douyin generally showed higher GQS distributions, whereas TikTok content clustered at lower scores; Bilibili demonstrated intermediate quality. Similar patterns were observed for reliability (Figure 2B). YouTube showed the highest concentration of modified DISCERN scores in the upper range, while Bilibili and Douyin displayed moderate reliability. In contrast, TikTok videos were concentrated in lower mDISCERN score ranges, indicating comparatively lower reliability. When examining scores based on video source, materials produced by CMP and OHI consistently clustered at the higher end of the GQS, implying reliable and high-quality content. The distribution for these professional sources was notably consistent. In sharp contrast, videos from PC scored significantly lower, with the vast majority of data points clustering around scores of 1 and 2 (Figure 2C), suggesting significant concerns regarding the accuracy of patient-generated content. Regarding reliability measured by mDISCERN (Figure 2D), both CMP and OHI achieved higher median scores, significantly outperforming the low scores recorded for PC and Other source types. When analyzing results based on content categories (Figure 2E), videos covering ‘Basics and Principles of Anesthesia’ generally achieved moderate-to-high GQS scores, indicating a focus on educational depth in this category. ‘Perioperative Process and Patient Experience’ and ‘Safety, Risks and Myth Busting’ also maintained acceptable quality levels. In terms of reliability assessment (Figure 2F), content focused on ‘Basics and Principles of Anesthesia’ distinguished itself by obtaining relatively higher reliability scores, whereas videos in the ‘The Anesthesia Profession’ category showed greater variance and generally lower reliability. Patient Utility Assessment Platform-level analysis of PEMAT-U scores (Figure 3A) showed higher understandability on YouTube and Douyin, with score distributions clustered toward the upper range. Bilibili demonstrated greater variability, whereas TikTok maintained relatively high scores with moderate variance. Actionability differed markedly across platforms (Figure 3B). TikTok and YouTube exhibited high actionability scores, with most videos approaching the upper limit, whereas Douyin and Bilibili clustered at lower score ranges, indicating limited actionable guidance. Analyses by video source showed higher understandability among CMP and OHI, while PC content displayed broader variation and lower median scores (Figure 3C). Actionability varied substantially across sources (Figure 3D), with CMP and OHI showing wide score distributions and PC and Other sources generally demonstrating lower utility. By thematic category, “Basics and Principles of Anesthesia” and “Perioperative Process” showed higher understandability (Figure 3E). Utility differed across themes (Figure 3F), with “Perioperative Process and Patient Experience” showing higher actionability compared with categories focused on professional narratives or theoretical content. Correlation and Stepwise Regression Analysis We further investigated interactions between variables through Spearman rank correlation analysis. Spearman rank correlation analysis demonstrated strong positive correlations among engagement metrics (likes, comments, and shares), whereas correlations between engagement and professional quality metrics (GQS and mDISCERN) were weak or non-significant (Figure 4). Video duration showed moderate positive correlations with both GQS and mDISCERN, and understandability was strongly correlated with GQS. Stepwise multivariable linear regression identified key predictors of video quality. The final model explained 43.3% of the variance in GQS scores (adjusted R 2 =0.433). Higher understandability (β=0.474), longer video duration (β=0.344), and greater actionability (β=0.095) were significantly associated with higher GQS scores (Table 2). No substantial multicollinearity was observed among predictors. Table 2. Multivariable stepwise linear regression analysis predicting Global Quality Score (GQS). Model Predictor Unstandardized coefficients Standardized coefficients t test (df) P value Collinearity statistics B (95% CI) SE β Tolerance VIF Model 1 (Constant) 0.675 (0.293 to 1.058) 0.195 3.469 (373) <0.001 PEMAT-U 0.036 (0.031 to 0.042) 0.003 0.558 12.998 (373) <0.001 1.000 1.000 Model 2 (Constant) 0.664 (0.284 to 1.045) 0.193 3.435 (372) <0.001 PEMAT-U 0.034 (0.028 to 0.040) 0.003 0.526 11.823 (372) <0.001 0.918 1.090 PEMAT-A 0.003 (0.001 to 0.005) 0.001 0.111 2.500 (372) 0.013 0.918 1.090 Model 3 (Constant) 0.661 (0.314 to 1.008) 0.177 3.747 (371) <0.001 PEMAT-U 0.031 (0.025 to 0.036) 0.003 0.474 11.544 (371) <0.001 0.898 1.113 PEMAT-A 0.002 (0.000 to 0.004) 0.001 0.095 2.333 (371) 0.020 0.916 1.092 Duration 0.001 (0.001 to 0.002) 0.000 0.344 8.697 (371) <0.001 0.970 1.030 Abbreviations: PEMAT-U, Patient Education Materials Assessment Tool–Understandability; PEMAT-A, Patient Education Materials Assessment Tool–Actionability; VIF, Variance Inflation Factor. A separate regression model identified determinants of reliability. The final model explained 38.4% of the variance (adjusted R 2 =0.384), with video duration (β=0.434) emerging as the strongest predictor, followed by understandability (β=0.306) and actionability (β=0.155) (Table 3). Collinearity statistics indicated stable model estimates. Table 3. Stepwise regression coefficients, statistical significance, and collinearity assessment (modified DISCERN). Model Predictor Unstandardized coefficients Standardized coefficients t test (df) P value Collinearity statistics B (95% CI) SE β Tolerance VIF Model 1 Duration 0.002 (0.002 to 0.003) 0 0.499 11.121 (373) <0.001 1 1 Model 2 Duration 0.002 (0.002 to 0.002) 0 0.441 10.552 (372) <0.001 0.615 1.627 PEMAT_U 0.024 (0.018 to 0.029) 0.003 0.349 8.344 (372) <0.001 0.615 1.627 Model 3 Duration 0.002 (0.002 to 0.002) 0 0.434 10.545 (371) <0.001 0.613 1.63 PEMAT_U 0.021 (0.015 to 0.027) 0.003 0.306 7.140 (371) <0.001 0.267 3.743 PEMAT_A 0.004 (0.002 to 0.006) 0.001 0.155 3.648 (371) <0.001 0.31 3.227 Abbreviations: PEMAT-U, Patient Education Materials Assessment Tool–Understandability; PEMAT-A, Patient Education Materials Assessment Tool–Actionability; VIF, Variance Inflation Factor. Comparative Analysis of Chinese vs. International Platforms To explore the impact of distinct digital ecosystems, we pooled data from Chinese platforms (Bilibili, Douyin; n=200) and international platforms (TikTok, YouTube; n=200) into Table 4. Mann-Whitney U test results revealed distinct content strategies and user engagement patterns. Table 4. Chinese vs. International platforms. Variable Chinese platforms (n=200) International platforms (n=200) Mann-Whitney U Duration (s) 110 (62.75–234.50) 61.50 (36.75–157.25) <0.001 Days since upload (d) 494 (117.50–1121.75) 781.50 (316–1387.25) <0.001 Likes 504.50 (52–3727) 2748 (241.75–19525) <0.001 Comments 56.50 (6–509.25) 96.50 (10.25–529.75) 0.275 Followers 14500 (1590.50–107250) 194000 (11725–864675) <0.001 GQS 3 (3–4) 3 (2–4) 0.147 mDISCERN 3 (2–3) 3 (2–3) 0.342 PEMAT-U 66.67 (57.64–75) 72.73 (62.50–82.20) <0.001 PEMAT-A 25 (0–54.17) 100 (66.67–100) <0.001 Note: Values are presented as median (IQR) unless otherwise indicated. Abbreviations: GQS, Global Quality Score; mDISCERN, modified DISCERN; PEMAT-U, Patient Education Materials Assessment Tool–Understandability; PEMAT-A, Patient Education Materials Assessment Tool–Actionability; IQR, Interquartile Range. Chinese platforms prioritized content depth, whereas international platforms favored brevity and high engagement. The median duration of videos on Chinese platforms was significantly longer than that on international platforms (110 seconds vs. 61.5 seconds, P<.001). Conversely, international platform videos garnered significantly higher user engagement, with a median like count of 2,748, far exceeding the Chinese platforms' 504.5 (P<.001), likely driven by the massive follower bases of international creators (median followers: 194,000 vs. 14,500, P<.001). In terms of content quality, no significant difference was observed in scientific accuracy. Both GQS (P=.147) and mDISCERN (P=.342) scores showed no statistical difference between the two groups, indicating that anesthesia educational information is at a comparable level of quality and reliability globally. However, a striking divergence was observed in utility. International platforms demonstrated superior understandability (PEMAT-U: 72.73% vs. 66.67%, P<.001), and the disparity was most profound in actionability (PEMAT-A). The median utility score for international platform videos reached 100% (IQR 66.67–100), whereas Chinese videos scored significantly lower (median 25%, IQR 0–54.17; P<.001). This suggests that while Chinese creators tend to explain theoretical mechanisms (longer duration), international creators excel at providing clear, executable operational guidelines in a concise format. Discussion Principal Findings This study provides the first cross-cultural, multi-platform assessment of anesthesia-related educational content. Consistent with findings in neurosurgery and oncology,[16, 24] our data indicate that the overall quality and reliability of anesthesia videos remain moderate globally. However, our most pivotal finding is the identification of a profound utility gap between distinct digital ecosystems. International platforms (TikTok/YouTube) prioritize actionability, effectively empowering patients with executable perioperative guidance. In sharp contrast, Chinese platforms (Douyin/Bilibili), while rich in theoretical mechanisms, exhibit a systemic deficiency in behavioral directives. This dichotomy likely reflects divergent medical communication cultures: the Western emphasis on Enhanced Recovery After Surgery (ERAS) and shared decision-making encourages patient participation,[31, 32] whereas Chinese content remains rooted in a traditional, didactic ‘lecture-style’ approach. Furthermore, the decoupling of user engagement from information quality—particularly evident on TikTok—reaffirms the ‘Popularity-Quality Paradox’,[33] alerting stakeholders that algorithmic virality is a poor proxy for accuracy of online medical information. Characterization of Video Attributes and Viewer Engagement Patterns The analysis of video attributes underscores significant divergences across digital ecosystems. Bilibili videos featured the longest median duration (180 seconds), consistent with its positioning as a ‘pan-knowledge community’ where creators favor exhaustive mechanistic explanations. However, according to the Cognitive Load Theory in multimedia learning, excessively long videos may exceed the information processing capacity of anxious patients.[34, 35] Conversely, the brevity of videos on TikTok and Douyin (approximately 60–70 seconds) aligns with fragmented consumption habits but risks oversimplifying complex medical information. More importantly, highly compressed video formats tend to prioritize concise expert-driven explanations over narrative or experiential storytelling, which may inadvertently constrain the representation of patient-centered perspectives. Within this context, we observed a conspicuous paucity of the ‘Patient Perspective’ across all platforms. Unlike chronic conditions such as diabetes where illness narratives are central to community building,[36] anesthesia is often perceived by patients as a transient, procedure-oriented service rather than a disease requiring long-term self-management. We hypothesize this is partly attributable to the ‘pharmacological unconsciousness’ inherent to general anesthesia;[37] patients lack subjective memory of the intraoperative period, precluding the formation of personal narratives. Consequently, the discourse power of anesthesia education remains predominantly professional-led, potentially limiting the emotional resonance required to effectively alleviate preoperative anxiety. This highlights a critical gap in current digital health communication strategies, suggesting that future perioperative educational content should deliberately integrate patient-oriented narratives to balance scientific accuracy with psychological reassurance. Video Ratings and Quality Evaluation In terms of quality assessment, YouTube demonstrated superior performance across both GQS and mDISCERN metrics, indicating greater informational depth and reliability. This advantage may be attributed less to geographic context and more to platform architecture: ecosystems that support longer-form content allow creators to provide more structured explanations, cite evidence, and disclose sources, all of which align with established standards for transparent medical communication.[38] By contrast, short-form video environments, regardless of region, may inherently constrain informational completeness. The reduced duration and algorithm-driven emphasis on engagement can encourage concise delivery at the expense of contextual nuance, citation transparency, and balanced discussion of uncertainties, potentially explaining lower reliability scores observed on other platforms. Importantly, differences were also evident across utility-related measures. According to the design philosophy of the PEMAT tool, educational materials that explicitly guide patient actions are more likely to enhance self-efficacy and informed participation.[29] Videos emphasizing conceptual explanations without translating knowledge into actionable steps may therefore achieve reasonable informational quality while remaining limited in practical usefulness for patients facing imminent surgery. These findings carry important implications for both content creators and digital health strategies. For medical professionals and institutional uploaders, high-quality perioperative education should not only prioritize accuracy but also incorporate clear action-oriented guidance tailored to patient decision-making needs.[39] For platform designers and public health communicators, the results highlight a structural tension between engagement-driven content ecosystems and the requirements of effective patient education.[40] If unresolved, this mismatch may perpetuate information environments in which patients are informed but insufficiently empowered, ultimately limiting the capacity of digital media to reduce preoperative uncertainty and anxiety. Correlation Analysis and Model Prediction Correlation analysis further substantiated the phenomenon of ‘misplaced trust’ on social media. While engagement metrics (likes, comments) showed strong internal consistency, they were uncoupled from professional quality standards (GQS/mDISCERN). This confirms that public endorsement is driven more by entertainment value or emotional resonance than by scientific rigor, posing a significant challenge in the context of the current ‘infodemic’.[23] Regression models provided critical insights for quality improvement. Understandability (PEMAT-U) emerged as the most potent predictor of overall quality (GQS), implying that regardless of professional depth, content is perceived as high-quality only when it is accessible. Intriguingly, video duration emerged as the strongest positive predictor of reliability. This finding forcefully refutes the ‘shorter is better’ dogma prevalent in the short-video era. For a discipline involving complex pharmacology and physiological risks, micro-videos (e.g., <60 seconds) often fail to convey a complete chain of scientific evidence and necessary risk disclosures.[41] This suggests a fundamental conflict between algorithmic preferences and medical pedagogy. Addressing this mismatch will likely require coordinated strategies at multiple levels. For content creators and medical institutions, designing modular educational formats, combining concise, engaging videos with links to longer evidence-based explanations, may help reconcile accessibility with informational integrity.[42] At the platform and policy level, greater emphasis on quality-sensitive recommendation mechanisms and credible source amplification could mitigate the dominance of purely engagement-driven metrics, thereby fostering digital health ecosystems that are both attention-compatible and educationally effective. Recommendations Based on Our Results In light of the cross-cultural disparities and quality predictors identified in this study, we propose a strategic realignment for future anesthesia education: Content creators may improve patient utility by incorporating clearer actionable guidance alongside educational information.[43] Clinicians may support patients by curating and recommending high-quality online resources rather than providing generic online search advice.[44] Platform administrators and regulatory bodies may consider integrating quality-sensitive mechanisms and professional oversight to reduce the dissemination of misinformation. In addition, as increasing numbers of individuals seek health information from large language model–based systems, future evaluations should extend beyond social media videos to include AI-generated health information.[45] This study carries significant clinical implications. Accurate and actionable preoperative video education has been proven to reduce anxiety and improve compliance.[46] However, the internet remains a double-edged sword; high-quality digital resources can optimize the physician-patient relationship, while misinformation may precipitate unnecessary conflict.[17] By identifying structural deficits in current educational content, anesthesiologists can develop targeted supplementary materials to fill online information gaps. This approach optimizes perioperative communication efficiency and improves pain management outcomes,[6] ultimately contributing to the realization of the patient-centered Perioperative Surgical Home.[47] Limitations This study has several limitations. First, as a cross-sectional analysis, the data represent a temporal snapshot and may not capture the dynamic evolution of video content and algorithm-driven visibility over time.[26] Second, the restriction to English and Chinese content limits the generalizability of findings to other linguistic or digital ecosystems that may differ in content conventions and user engagement patterns. Third, although a standardized keyword-based search strategy was employed, this approach may not fully reflect the passive information reception experience of real users within algorithmic recommendation feeds.[40] Finally, despite the use of independent dual raters, the PEMAT and mDISCERN tools remain semi-subjective, and ratings may still be influenced by cultural or contextual perspectives. Conclusions This study provides the first cross-cultural, multidimensional quality assessment of anesthesia-related videos on Bilibili, Douyin, TikTok, and YouTube. Results indicate that the overall quality of current content is merely moderate, with significant disparities across digital ecosystems. Our core finding unveils a profound utility gap: international platforms prioritize actionable perioperative behavioral guidance, whereas Chinese platforms are rich in theory but deficient in practical patient instructions. Furthermore, the ‘Popularity-Quality Paradox’ is particularly pronounced on algorithm-driven short-video platforms. Future anesthesia education requires a paradigm shift. Creators must evolve from mechanism explanation to behavioral intervention by integrating actionable checklists to enhance clinical utility. Platform regulators should introduce quality-based algorithmic weighting to sanitize the information ecology. Crucially, patients must adopt a multi-source verification strategy and avoid blind trust in high-engagement content. Only through such multi-stakeholder collaboration can we bridge the cognitive gap and achieve true information empowerment and patient safety in the perioperative period. Abbreviations GQS: Global Quality Score. CMP: Certified Medical Professionals. OHI: Official Health Institutions. SC: Science Communicators. PC: Patients and Caregivers. mDISCERN: modified DISCERN. PEMAT: Patient Education Materials Assessment Tool. PEMAT-U: Patient Education Materials Assessment Tool–Understandability. PEMAT-A: Patient Education Materials Assessment Tool–Actionability. ERAS: Enhanced Recovery After Surgery. IQR: Interquartile Range. VIF: Variance Inflation Factor. Declarations Ethics approval and consent to participate: This study used publicly available online video data and did not involve clinical records, human biological samples, or direct interaction with human participants. Therefore, ethics approval and informed consent were not required. Consent for publication: Not applicable. Availability of data and materials: All data generated or analyzed during this study are included in this published paper and its multimedia appendices (Multimedia Appendix 1). The raw video and comment data analyzed are publicly available on the Bilibili, Douyin, YouTube, and TikTok, subject to their terms of service and content availability. Competing Interest: The authors declare that they have no competing interests. Funding: This study was funded by the National Natural Science Foundation of China (72574153 to PL, 72204174 to PL), the China Postdoctoral Science Foundation (2022M722262 to PL), the Postdoctoral Program of Sichuan University (2024SCU12026 to PL), the Postdoctoral Program of West China Hospital, Sichuan University (2023HXBH009 to PL), the 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC21008 to TZ), the Sichuan Province Natural Science Foundation of China (2023NSFSC0512 to TZ), and the CAMS Innovation Fund for Medical Sciences (2023-I2M-C&T-B-122 to TZ). Authors’ Contributions:JC and GW conceived and designed the study. JC and GW were responsible for reviewing and scoring the videos. JC and GW collected and analyzed the data. GW prepared all tables and JC prepared all figures. JC, GW, and TZ wrote the original draft. PL, XH, and TZ reviewed the manuscript and provided critical revisions to the intellectual content. 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Supplementary Files MultimediaAppendix1.Datasetonanesthesiarelatedvideos.xlsx Multimedia Appendix 1: Dataset on anesthesia-related videos MultimediaAppendix2.Dunntestacrossdifferentplatforms.docx Multimedia Appendix 2: Dunn test across different platforms MultimediaAppendix3.docx Multimedia Appendix 3: Characteristics of the videos across sources MultimediaAppendix4.docx Multimedia Appendix 4: Percentage distribution of anesthesia education videos by source and content on different platforms MultimediaAppendix5.docx Multimedia Appendix 5: Stepwise regression analysis (Global Quality Score) MultimediaAppendix6.docx Multimedia Appendix 6: Stepwise regression analysis (modified DISCERN) Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-9635247","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636898420,"identity":"56f0e40c-54e7-4079-b7a5-f986846f1817","order_by":0,"name":"Jianwen Cai","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jianwen","middleName":"","lastName":"Cai","suffix":""},{"id":636898421,"identity":"8066131d-230e-40b4-b31b-ed3e58cbc2a8","order_by":1,"name":"Gang Wang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Wang","suffix":""},{"id":636898422,"identity":"f7a85cae-7930-46a1-8323-e7fd43c83cca","order_by":2,"name":"Tianqi Zhang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Tianqi","middleName":"","lastName":"Zhang","suffix":""},{"id":636898423,"identity":"335bbdbd-1034-4fa2-a2c5-bb56101a0a6f","order_by":3,"name":"Tao Zhu","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhu","suffix":""},{"id":636898424,"identity":"5a14540a-ec52-406d-8c6a-e2dbed16d6c9","order_by":4,"name":"Peiyi Li","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Peiyi","middleName":"","lastName":"Li","suffix":""},{"id":636898425,"identity":"72c4136c-2b6b-4ea2-b101-3420f38dad58","order_by":5,"name":"Xuechao Hao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYJACZiCWY2NvP0CaFmM+njMJpGlJnCfhYECccv7ZzYc/F7bZpbdJMCQw/KjYRliLxJ1jadIzziTntkk3HmDsOXObCGtu5Jgx81QcyG2TOZDAzNhGhBb5G/mfP/MYHEhnk0gwIE6LwY0cBmmgLQnEazG8kWYmzXMm2bANGMgHifKL3I3kx5952+zk5dvbDz74UUGM95HBARLVj4JRMApGwSjABQAnQzjX8CqRpgAAAABJRU5ErkJggg==","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Xuechao","middleName":"","lastName":"Hao","suffix":""}],"badges":[],"createdAt":"2026-05-06 22:24:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9635247/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9635247/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108978393,"identity":"9ec797a4-a111-4ed5-8a36-fc9baac73061","added_by":"auto","created_at":"2026-05-11 11:37:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":367440,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design and overall framework. Created in BioRender. A, Flowchart for searching and screening anesthesia education videos. B, Data Extraction and Classification. C, Quality and Utility Assessments.\u003c/p\u003e","description":"","filename":"Figure1.Studydesignandoverallframework.png","url":"https://assets-eu.researchsquare.com/files/rs-9635247/v1/d997e59eb219207fd2f0e04a.png"},{"id":108978329,"identity":"92986c9a-c8a8-412b-ba8c-3eb8665246b5","added_by":"auto","created_at":"2026-05-11 11:36:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":410060,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal Quality Score (GQS) and modified DISCERN (mDISCERN) across platforms, sources, and content categories. A-B, Distribution of GQS (A) and mDISCERN (B) scores across social media platforms. C-D, Distribution of GQS (C) and mDISCERN (D) scores according to video source categories. E-F, Distribution of GQS (E) and mDISCERN (F) scores across video thematic categories.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9635247/v1/007899421d7f7f24266d5c8d.png"},{"id":109067569,"identity":"51157d38-4368-47f5-9c6e-e01c2cd73c5a","added_by":"auto","created_at":"2026-05-12 09:56:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":447417,"visible":true,"origin":"","legend":"\u003cp\u003ePEMAT understandability (PEMAT-U) and actionability (PEMAT-A) across platforms, sources, and content categories. A-B, Distribution of PEMAT-U (A) and PEMAT-A (B) scores across social media platforms. C-D, Distribution of PEMAT-U (C) and PEMAT-A (D) scores according to video source categories. E-F, Distribution of PEMAT-U (E) and PEMAT-A (F) scores across thematic content categories.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9635247/v1/22033bdc91e0038516d80d78.png"},{"id":108978431,"identity":"3a041b2c-5b94-4fe4-89bc-637deca79656","added_by":"auto","created_at":"2026-05-11 11:37:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":358112,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlation analysis among video metrics and quality scores. Heatmap showing Spearman correlation coefficients among engagement metrics, video characteristics, and quality assessment scores, including GQS, mDISCERN, PEMAT-U, and PEMAT-A.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9635247/v1/f036a9a1c393d10441da7c2e.png"},{"id":109070182,"identity":"0f6c8ed1-b1c8-42b1-9038-ada7fe815470","added_by":"auto","created_at":"2026-05-12 10:29:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1992997,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9635247/v1/ea562174-bc6a-4765-bc86-70aac84da6ba.pdf"},{"id":108978278,"identity":"93a3e58f-c0c8-44f2-a7e5-23b10049b460","added_by":"auto","created_at":"2026-05-11 11:35:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":57374,"visible":true,"origin":"","legend":"\u003cp\u003eMultimedia Appendix 1: Dataset on anesthesia-related videos\u003c/p\u003e","description":"","filename":"MultimediaAppendix1.Datasetonanesthesiarelatedvideos.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9635247/v1/e325a7ca1eec07d983131640.xlsx"},{"id":108978364,"identity":"c75f663d-ffed-4a1d-82ca-2036af6d3be3","added_by":"auto","created_at":"2026-05-11 11:36:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20877,"visible":true,"origin":"","legend":"\u003cp\u003eMultimedia Appendix 2: Dunn test across different platforms\u003c/p\u003e","description":"","filename":"MultimediaAppendix2.Dunntestacrossdifferentplatforms.docx","url":"https://assets-eu.researchsquare.com/files/rs-9635247/v1/ee1d8e40d39af2c0a8e0cb7f.docx"},{"id":108978430,"identity":"a426c1ba-daf3-457c-bf58-9a29c7b4b7f7","added_by":"auto","created_at":"2026-05-11 11:37:37","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15962,"visible":true,"origin":"","legend":"\u003cp\u003eMultimedia Appendix 3: Characteristics of the videos across sources\u003c/p\u003e","description":"","filename":"MultimediaAppendix3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9635247/v1/b93c7179c5845d3ea0af82f6.docx"},{"id":108978406,"identity":"b2ba4dd0-5543-4c54-a55d-e5d1a8397376","added_by":"auto","created_at":"2026-05-11 11:37:12","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16212,"visible":true,"origin":"","legend":"\u003cp\u003eMultimedia Appendix 4: Percentage distribution of anesthesia education videos by source and content on different platforms\u003c/p\u003e","description":"","filename":"MultimediaAppendix4.docx","url":"https://assets-eu.researchsquare.com/files/rs-9635247/v1/b998cd6219fd377813a963b3.docx"},{"id":108978330,"identity":"d36a8d0b-0348-4c5b-8b89-bc0acc8c7c03","added_by":"auto","created_at":"2026-05-11 11:36:24","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14448,"visible":true,"origin":"","legend":"\u003cp\u003eMultimedia Appendix 5: Stepwise regression analysis (Global Quality Score)\u003c/p\u003e","description":"","filename":"MultimediaAppendix5.docx","url":"https://assets-eu.researchsquare.com/files/rs-9635247/v1/529528589bdfce687305912a.docx"},{"id":108978324,"identity":"7b35f8d7-9d07-4b60-8b9c-4278390f35c6","added_by":"auto","created_at":"2026-05-11 11:36:19","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":14582,"visible":true,"origin":"","legend":"\u003cp\u003eMultimedia Appendix 6: Stepwise regression analysis (modified DISCERN)\u003c/p\u003e","description":"","filename":"MultimediaAppendix6.docx","url":"https://assets-eu.researchsquare.com/files/rs-9635247/v1/ef0f5819e5e63e03b5b3bb14.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quality, Reliability, and Patient Educational Utility of Anesthesia-Related Videos Across Global Social Media Platforms: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to estimates from the Lancet, approximately 313\u0026nbsp;million surgical procedures are undertaken globally each year, suggesting that a substantial proportion of the world’s population experiences perioperative care annually.[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] Preoperative anxiety represents a pervasive challenge in global perioperative medicine, with reported prevalence rates ranging from 60% to 80% among elective surgical patients.[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e] This psychological stress stems from fear of the unknown, concerns regarding pain, and catastrophic ideation such as failure to awaken or intraoperative awareness.[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e] Clinical evidence indicates that severe preoperative anxiety is not merely a negative emotional experience but a catalyst for significant pathophysiological alterations[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] Furthermore, elevated anxiety levels are significantly correlated with delayed postoperative recovery, impaired wound healing, and diminished patient satisfaction.[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] Consequently, the effective identification and mitigation of preoperative anxiety are paramount for ensuring perioperative safety and improving clinical outcomes.\u003c/p\u003e \u003cp\u003eIn the traditional medical model, the mitigation of preoperative anxiety relies predominantly on preoperative visits by anesthesiologists and printed educational materials.[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e] Although face-to-face communication is considered the gold standard, this intervention faces severe challenges in increasingly busy clinical practices. Studies have shown that due to time constraints in outpatient settings and the scarcity of medical resources, physicians are often unable to fully explain complex anesthetic mechanisms and risks within limited timeframes.[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] Simultaneously, constrained by health literacy levels, patient retention of verbal information is remarkably low; approximately 40%–80% of medical information is forgotten or misunderstood immediately after the consultation concludes.[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] While printed leaflets serve as supplements, they frequently lack readability due to obscure professional terminology, failing to effectively reach the patient's cognitive core.[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] This asymmetry between supply and demand necessitates more efficient forms of education to bridge the information gap.\u003c/p\u003e \u003cp\u003eWith the ubiquity of the mobile internet, a paradigm shift has occurred in how the public acquires health information. An increasing number of patients are turning to social media platforms for medical advice, where short-form videos, characterized by their vividness, fragmentation, and accessibility, have rapidly become a primary information source for the global public.[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] Although these platforms offer opportunities to fill the information vacuum, the scientific validity of their content remains largely unregulated. On one hand, social media algorithms typically prioritize content with high interaction metrics over high-quality evidence-based medical information;[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] on the other hand, a proliferation of low-quality content generated by non-professionals often contains misinformation, such as claims that general anesthesia damages the brain or that neuraxial anesthesia leads to paralysis.[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] This ‘infodemic’ fails to alleviate anxiety and may instead exacerbate patient panic,[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e] potentially leading patients to refuse necessary analgesia or anesthesia techniques due to misconceptions, thereby seriously interfering with standard clinical decision-making.[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAlthough previous studies have utilized tools such as DISCERN and the Global Quality Score (GQS) to evaluate online health information quality in fields like orthopedics, dermatology, and oncology,[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] there is a lack of systematic quality assessment specific to anesthesiology, a highly specialized field involving complex pharmacological mechanisms. In particular, current literature is mostly limited to single platforms or single linguistic environments, with few studies simultaneously including mainstream video platforms from different cultural backgrounds to comparatively analyze the quality differences and dissemination characteristics of anesthesia education information in the global digital ecosystem.[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] In light of this, this study aims to employ a multidimensional assessment system to comprehensively evaluate the quality, reliability, and utility of anesthesia-related short videos on four globally representative video platforms (Bilibili, Douyin, TikTok, and YouTube). Our objectives are not only to facilitate more targeted engagement with perioperative online information among both patients and anesthesiologists, but also to generate evidence-based insights to support global healthcare professionals in optimizing digital health education strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eEthical Considerations\u003c/p\u003e\u003cp\u003eAll data for this study were obtained from publicly accessible videos on Bilibili, Douyin, TikTok, and YouTube. The research did not involve any clinical records, human biological samples, or animal experimental data. As the study relied exclusively on public, anonymized data and involved no direct interaction or intervention with platform users, it was deemed exempt from institutional review board (IRB) approval in accordance with international ethical standards and institutional regulations. Consequently, informed consent was not required.\u003c/p\u003e\u003cp\u003ePlatforms and Search Strategy\u003c/p\u003e\u003cp\u003eThis cross-sectional study included four major global video-sharing platforms: Bilibili, Douyin, TikTok, and YouTube. Data collection was conducted from November 10 to November 15, 2025. To mitigate potential bias introduced by personalized recommendation algorithms, all searches were performed using the incognito or private mode of web browsers. Prior to each search session, browser caches and history were cleared, and no personal accounts were logged in. A bilingual keyword strategy was employed to ensure comprehensive coverage of both Chinese and international platforms. Search term combinations included: ‘麻醉’ (Anesthesia), ‘全麻’ (General Anesthesia), ‘麻醉科普’ (Anesthesia Science Popularization), and ‘麻醉医生’ (Anesthesiologist) in Chinese platforms (Bilibili, Douyin); ‘anesthesia’, ‘anesthesiologist’, ‘general anesthesia’, and ‘what is anesthesia’ in English platforms (YouTube, TikTok). For each keyword on every platform, the top videos ranked by the platform's default sorting algorithm (YouTube: ‘Relevance’; TikTok: ‘Top’; Bilibili and Douyin: ‘Comprehensive’) were extracted. Inclusion criteria were: (1) primary content related to anesthesia education targeted at the general public and medical professionals; (2) language in Mandarin (Bilibili/Douyin) or English (YouTube/TikTok); and (3) video format (excluding static image slides). Exclusion criteria included: (1) duplicate videos (within platforms); (2) irrelevant content (e.g., surgical footage, music videos, news reports); (3) News, Ads, or promotions; and (4) extremely poor audio or visual quality precluding assessment. Video screening based on the predefined inclusion and exclusion criteria was independently performed by one researcher. Finally, 100 videos were selected based on the sorting principles of each platform (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eData Extraction and Classification\u003c/p\u003e\u003cp\u003eKey metadata for each video in the final dataset were systematically recorded, including uploader source, duration, and user interaction metrics (likes, comments, and total followers). The dataset was organized and stored using Microsoft Excel (Multimedia Appendix 1).\u003c/p\u003e\u003cp\u003eTo facilitate systematic analysis, a video classification scheme was implemented. Videos were categorized into four thematic groups based on their primary subject matter: (1) The Anesthesia Profession (the role, responsibilities, working environment, and professional identity of anesthesiologists); (2) Safety, Risks, and Myth Busting (addressing anesthesia-related safety issues, potential risks or complications, and myth-busting information intended to clarify misconceptions and answer common public concerns); (3) Perioperative Process and Patient Experience (perioperative journey from the patient’s perspective, including preparation, procedural experiences, and postoperative recovery); and (4) Basics and Principles of Anesthesia (educational content explaining fundamental concepts, mechanisms, and basic medical principles of anesthesia). Furthermore, information sources were classified into five types: (1) Certified Medical Professionals (CMP); (2) Official Health Institutions (OHI); (3) Science Communicators (SC); (4) Patients and Caregivers (PC); and (5) Other/Non-Professional Media (accounts not fitting the above categories, such as entertainment channels). The two researchers jointly completed the data extraction and video topic classification. In cases of disagreement between the two researchers, a third reviewer (PL) served as an arbitrator and assigned the final classification (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eQuality and Utility Assessments\u003c/p\u003e\u003cp\u003eAll videos were collected and downloaded by a single researcher and were independently assessed and scored by two trained raters across all platforms using the predefined scoring instruments, including Global Quality Score (GQS), modified DISCERN (mDISCERN), and Patient Education Materials Assessment Tool (PEMAT), the details of these instruments were followed:\u003c/p\u003e\u003cp\u003e \u003cem\u003eGlobal Quality Score (GQS)\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eA 5-point scale used to evaluate the overall informational quality and flow of the video (1 = poor quality, content completely useless; 5 = excellent quality, rigorous logic, comprehensive content, and highly useful).\u003c/p\u003e\u003cp\u003e \u003cem\u003eModified DISCERN (mDISCERN)\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eA 5-point cumulative scale (0–5) used to assess content reliability based on five binary (Yes = 1/No = 0) questions: (1) Is the video's purpose clear? (2) Are reliable information sources cited? (3) Is the content balanced and unbiased? (4) Are additional information sources provided for patient reference? (5) Are areas of uncertainty mentioned?\u003c/p\u003e\u003cp\u003e \u003cem\u003ePatient Education Materials Assessment Tool (PEMAT)\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eA core instrument for evaluating health education materials from the patient's perspective. It comprises two domains: Understandability (PEMAT-U): Calculated based on 13 items (e.g., language, organization, layout, visual aids) to measure how easily the audience can understand the material (score: 0–100%). Actionability (PEMAT-A): Calculated based on 4 items (e.g., clear action instructions, provision of tools) to measure the material's effectiveness in empowering patients to take action (score: 0–100%).\u003c/p\u003e\u003cp\u003ePrior to formal independent scoring, both raters used the same predefined scoring criteria and independently evaluated ten videos from each platform that were not included in the final analysis to calibrate scoring interpretation and ensure cross-platform consistency. Interrater and intrarater reliability were assessed using the intraclass correlation coefficient (ICC).[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] The results demonstrated good agreement, with ICC values exceeding 0.70 across platforms and scoring rounds, indicating acceptable rating consistency. Following independent scoring, discrepancies were resolved through structured discussion based on predefined scoring criteria and video content. When disagreement persisted, a third rater served as an arbitrator and assigned the final scores. Final ratings were confirmed by consensus among all authors (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData normality was assessed using the Shapiro-Wilk test, which indicated non-parametric distributions. Consequently, key variables were summarized descriptively using medians and interquartile ranges (IQRs). Inter-group differences were evaluated using the Mann-Whitney U test, with Dunn’s test employed for pairwise post-hoc comparisons. Inter-rater reliability was assessed by calculating Cohen’s kappa coefficient. Spearman and Pearson correlation analyses were conducted to explore relationships between variables. Stepwise multivariable linear regression were developed to evaluate the impact of video variables on predicting video quality and reliability. A P-value of \u0026lt; .05 was considered statistically significant. All data analyses and model construction were performed using Python (version 3.12.7).\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eVideo Characteristics\u003c/h3\u003e\n\u003cp\u003eThe general characteristics of the analyzed videos are summarized in Table 1. The results indicated statistically significant differences across platforms in terms of user engagement metrics as well as assessed content quality, reliability, and utility scores (all P \u0026lt;.002). Consequently, Dunn\u0026rsquo;s test was utilized for pairwise post-hoc comparisons to delineate specific inter-platform differences. To control for the increased risk of Type I error associated with multiple comparisons, adjusted P-values were calculated. Detailed results of these post-hoc comparisons are provided in Multimedia Appendix 2. As detailed in Multimedia Appendix 3 and Multimedia Appendix 4, the distribution of video sources and content themes varied significantly.\u003c/p\u003e\n\u003cp\u003eTable 1. Characteristics and evaluation scores of anesthesia-related videos across platforms.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"108%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eBilibili\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eDouyin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eTikTok\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eYouTube\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eKruskal-Wallis H test (df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eAsymptotic significance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eDuration (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e180 (98\u0026ndash;354)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e71 (55\u0026ndash;123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e59 (42\u0026ndash;87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e86 (32\u0026ndash;380)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e65.743 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eDays since upload (d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e766 (320\u0026ndash;1320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e222 (67\u0026ndash;610)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e564 (282\u0026ndash;973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e1014 (455\u0026ndash;1936)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e66.704 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eLikes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e182 (32\u0026ndash;2688)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e983 (109\u0026ndash;4192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e4442 (804\u0026ndash;27950)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e2225 (103\u0026ndash;10675)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e38.649 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eComments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e18 (2\u0026ndash;282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e122 (13\u0026ndash;711)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e136 (28\u0026ndash;535)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e69 (3\u0026ndash;482)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e18.423 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eFollowers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e5034 (406\u0026ndash;110250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e26000 (5326\u0026ndash;104250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e1598 (797\u0026ndash;4636)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e186000 (2190\u0026ndash;588500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e56.379 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eGQS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e3 (3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e3 (3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e3 (3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e15.181 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003emDISCERN\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e3 (3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e24.097 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003ePEMAT-U\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e58.3 (50.0\u0026ndash;72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e72.7 (63.6\u0026ndash;77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e69.2 (58.3\u0026ndash;76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e75.0 (63.6\u0026ndash;84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e41.959 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003ePEMAT-A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e25.0 (0.0\u0026ndash;66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e0.0 (0.0\u0026ndash;50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e100.0 (100.0\u0026ndash;100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e100.0 (66.7\u0026ndash;100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e178.001 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.433%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Values are presented as median (IQR) unless otherwise indicated.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GQS, Global Quality Score; mDISCERN, modified DISCERN; PEMAT-U, Patient Education Materials Assessment Tool\u0026ndash;Understandability; PEMAT-A, Patient Education Materials Assessment Tool\u0026ndash;Actionability; IQR, Interquartile Range.\u003c/p\u003e\n\u003cp\u003eCMP were the predominant source type (222/400, 55.5%), with the highest proportion observed on TikTok (80%), followed by Douyin (58%) and YouTube (52%). In contrast, Bilibili showed a lower proportion of CMP-generated videos (32%) and relatively higher representation of SCs (28%) and Other sources (27%). Videos produced by PC were rare across all platforms (0%\u0026ndash;3%). Regarding content themes, \u0026ldquo;Basics and Principles of Anesthesia\u0026rdquo; was the most prevalent category, accounting for approximately half or more of videos across platforms, including TikTok (76%) and YouTube (75%). \u0026ldquo;The Anesthesia Profession\u0026rdquo; also appeared frequently, particularly on Douyin (22%).\u003c/p\u003e\n\u003cp\u003ePost-hoc analyses revealed substantial disparities in video characteristics across platforms. Specifically, Bilibili videos exhibited a significantly longer median duration (180 seconds, IQR 98\u0026ndash;354), far exceeding content on TikTok (59 seconds, P\u0026lt;.001) and Douyin (71 seconds, P\u0026lt;.001). In terms of user engagement, TikTok demonstrated exceptionally high interactivity, with a median like count of 4,442 (IQR 804\u0026ndash;27,950), which was significantly higher than that of Bilibili (182, P\u0026lt;.001) and Douyin (983, P\u0026lt;.001). However, regarding utility (PEMAT-A), YouTube and TikTok stood out with high scores, both achieving a median of 100% (IQR 66.7%\u0026ndash;100%), whereas Chinese platforms Douyin (0%) and Bilibili (25%) scored significantly lower (both P\u0026lt;.001). This suggests that although Bilibili provides more detailed (longer duration) information, international platforms outperform them in translating information into actionable patient guidance.\u003c/p\u003e\n\u003ch3\u003eQuality and Reliability Assessment\u003c/h3\u003e\n\u003cp\u003eInter-platform comparisons demonstrated distinct quality profiles (Figure 2A). Videos on YouTube and Douyin generally showed higher GQS distributions, whereas TikTok content clustered at lower scores; Bilibili demonstrated intermediate quality. Similar patterns were observed for reliability (Figure 2B). YouTube showed the highest concentration of modified DISCERN scores in the upper range, while Bilibili and Douyin displayed moderate reliability. In contrast, TikTok videos were concentrated in lower mDISCERN score ranges, indicating comparatively lower reliability.\u003c/p\u003e\n\u003cp\u003eWhen examining scores based on video source, materials produced by CMP and OHI consistently clustered at the higher end of the GQS, implying reliable and high-quality content. The distribution for these professional sources was notably consistent. In sharp contrast, videos from PC scored significantly lower, with the vast majority of data points clustering around scores of 1 and 2 (Figure 2C), suggesting significant concerns regarding the accuracy of patient-generated content. Regarding reliability measured by mDISCERN (Figure 2D), both CMP and OHI achieved higher median scores, significantly outperforming the low scores recorded for PC and Other source types.\u003c/p\u003e\n\u003cp\u003eWhen analyzing results based on content categories (Figure 2E), videos covering \u0026lsquo;Basics and Principles of Anesthesia\u0026rsquo; generally achieved moderate-to-high GQS scores, indicating a focus on educational depth in this category. \u0026lsquo;Perioperative Process and Patient Experience\u0026rsquo; and \u0026lsquo;Safety, Risks and Myth Busting\u0026rsquo; also maintained acceptable quality levels. In terms of reliability assessment (Figure 2F), content focused on \u0026lsquo;Basics and Principles of Anesthesia\u0026rsquo; distinguished itself by obtaining relatively higher reliability scores, whereas videos in the \u0026lsquo;The Anesthesia Profession\u0026rsquo; category showed greater variance and generally lower reliability.\u003c/p\u003e\n\u003ch3\u003ePatient Utility Assessment\u003c/h3\u003e\n\u003cp\u003ePlatform-level analysis of PEMAT-U scores (Figure 3A) showed higher understandability on YouTube and Douyin, with score distributions clustered toward the upper range. Bilibili demonstrated greater variability, whereas TikTok maintained relatively high scores with moderate variance. Actionability differed markedly across platforms (Figure 3B). TikTok and YouTube exhibited high actionability scores, with most videos approaching the upper limit, whereas Douyin and Bilibili clustered at lower score ranges, indicating limited actionable guidance.\u003c/p\u003e\n\u003cp\u003eAnalyses by video source showed higher understandability among CMP and OHI, while PC content displayed broader variation and lower median scores (Figure 3C). Actionability varied substantially across sources (Figure 3D), with CMP and OHI showing wide score distributions and PC and Other sources generally demonstrating lower utility.\u003c/p\u003e\n\u003cp\u003eBy thematic category, \u0026ldquo;Basics and Principles of Anesthesia\u0026rdquo; and \u0026ldquo;Perioperative Process\u0026rdquo; showed higher understandability (Figure 3E). Utility differed across themes (Figure 3F), with \u0026ldquo;Perioperative Process and Patient Experience\u0026rdquo; showing higher actionability compared with categories focused on professional narratives or theoretical content.\u003c/p\u003e\n\u003ch3\u003eCorrelation and Stepwise Regression Analysis\u003c/h3\u003e\n\u003cp\u003eWe further investigated interactions between variables through Spearman rank correlation analysis. Spearman rank correlation analysis demonstrated strong positive correlations among engagement metrics (likes, comments, and shares), whereas correlations between engagement and professional quality metrics (GQS and mDISCERN) were weak or non-significant (Figure 4). Video duration showed moderate positive correlations with both GQS and mDISCERN, and understandability was strongly correlated with GQS.\u003c/p\u003e\n\u003cp\u003eStepwise multivariable linear regression identified key predictors of video quality. The final model explained 43.3% of the variance in GQS scores (adjusted R\u003csup\u003e2\u003c/sup\u003e=0.433). Higher understandability (\u0026beta;=0.474), longer video duration (\u0026beta;=0.344), and greater actionability (\u0026beta;=0.095) were significantly associated with higher GQS scores (Table 2). No substantial multicollinearity was observed among predictors.\u003c/p\u003e\n\u003cp\u003eTable 2. Multivariable stepwise linear regression analysis predicting Global Quality Score (GQS).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eUnstandardized coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandardized coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et test (df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eCollinearity statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.675 (0.293 to 1.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.469 (373)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePEMAT-U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.036 (0.031 to 0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.998 (373)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.664 (0.284 to 1.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.435 (372)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePEMAT-U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.034 (0.028 to 0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.823 (372)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePEMAT-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003 (0.001 to 0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.500 (372)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.661 (0.314 to 1.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.747 (371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePEMAT-U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.031 (0.025 to 0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.544 (371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePEMAT-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002 (0.000 to 0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.333 (371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001 (0.001 to 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.697 (371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: PEMAT-U, Patient Education Materials Assessment Tool\u0026ndash;Understandability; \u0026nbsp;PEMAT-A, Patient Education Materials Assessment Tool\u0026ndash;Actionability; VIF, Variance Inflation Factor.\u003c/p\u003e\n\u003cp\u003eA separate regression model identified determinants of reliability. The final model explained 38.4% of the variance (adjusted R\u003csup\u003e2\u003c/sup\u003e=0.384), with video duration (\u0026beta;=0.434) emerging as the strongest predictor, followed by understandability (\u0026beta;=0.306) and actionability (\u0026beta;=0.155) (Table 3). Collinearity statistics indicated stable model estimates.\u003c/p\u003e\n\u003cp\u003eTable 3. Stepwise regression coefficients, statistical significance, and collinearity assessment (modified DISCERN).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eUnstandardized coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandardized coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et test (df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eCollinearity statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002 (0.002 to 0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.121 (373)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002 (0.002 to 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.552 (372)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePEMAT_U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.024 (0.018 to 0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.344 (372)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002 (0.002 to 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.545 (371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePEMAT_U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.021 (0.015 to 0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.140 (371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePEMAT_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004 (0.002 to 0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.648 (371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: PEMAT-U, Patient Education Materials Assessment Tool\u0026ndash;Understandability; \u0026nbsp;PEMAT-A, Patient Education Materials Assessment Tool\u0026ndash;Actionability; VIF, Variance Inflation Factor.\u003c/p\u003e\n\u003ch3\u003eComparative Analysis of Chinese vs. International Platforms\u003c/h3\u003e\n\u003cp\u003eTo explore the impact of distinct digital ecosystems, we pooled data from Chinese platforms (Bilibili, Douyin; n=200) and international platforms (TikTok, YouTube; n=200) into Table 4. Mann-Whitney U test results revealed distinct content strategies and user engagement patterns.\u003c/p\u003e\n\u003cp\u003eTable 4. Chinese vs. International platforms.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eChinese platforms (n=200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eInternational platforms (n=200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eMann-Whitney U\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDuration (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e110 (62.75\u0026ndash;234.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e61.50 (36.75\u0026ndash;157.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDays since upload (d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e494 (117.50\u0026ndash;1121.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e781.50 (316\u0026ndash;1387.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eLikes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e504.50 (52\u0026ndash;3727)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e2748 (241.75\u0026ndash;19525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eComments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e56.50 (6\u0026ndash;509.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e96.50 (10.25\u0026ndash;529.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eFollowers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e14500 (1590.50\u0026ndash;107250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e194000 (11725\u0026ndash;864675)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eGQS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e3 (3\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e3 (2\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003emDISCERN\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePEMAT-U\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e66.67 (57.64\u0026ndash;75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e72.73 (62.50\u0026ndash;82.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePEMAT-A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e25 (0\u0026ndash;54.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e100 (66.67\u0026ndash;100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Values are presented as median (IQR) unless otherwise indicated.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GQS, Global Quality Score; mDISCERN, modified DISCERN; PEMAT-U, Patient Education Materials Assessment Tool\u0026ndash;Understandability; PEMAT-A, Patient Education Materials Assessment Tool\u0026ndash;Actionability; IQR, Interquartile Range.\u003c/p\u003e\n\u003cp\u003eChinese platforms prioritized content depth, whereas international platforms favored brevity and high engagement. The median duration of videos on Chinese platforms was significantly longer than that on international platforms (110 seconds vs. 61.5 seconds, P\u0026lt;.001). Conversely, international platform videos garnered significantly higher user engagement, with a median like count of 2,748, far exceeding the Chinese platforms\u0026apos; 504.5 (P\u0026lt;.001), likely driven by the massive follower bases of international creators (median followers: 194,000 vs. 14,500, P\u0026lt;.001). In terms of content quality, no significant difference was observed in scientific accuracy. Both GQS (P=.147) and mDISCERN (P=.342) scores showed no statistical difference between the two groups, indicating that anesthesia educational information is at a comparable level of quality and reliability globally.\u003c/p\u003e\n\u003cp\u003eHowever, a striking divergence was observed in utility. International platforms demonstrated superior understandability (PEMAT-U: 72.73% vs. 66.67%, P\u0026lt;.001), and the disparity was most profound in actionability (PEMAT-A). The median utility score for international platform videos reached 100% (IQR 66.67\u0026ndash;100), whereas Chinese videos scored significantly lower (median 25%, IQR 0\u0026ndash;54.17; P\u0026lt;.001). This suggests that while Chinese creators tend to explain theoretical mechanisms (longer duration), international creators excel at providing clear, executable operational guidelines in a concise format.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrincipal Findings\u003c/p\u003e\n\u003cp\u003eThis study provides the first cross-cultural, multi-platform assessment of anesthesia-related educational content. Consistent with findings in neurosurgery and oncology,[16, 24]\u0026nbsp;our data indicate that the overall quality and reliability of anesthesia videos remain moderate globally. However, our most pivotal finding is the identification of a profound utility gap between distinct digital ecosystems. International platforms (TikTok/YouTube) prioritize actionability, effectively empowering patients with executable perioperative guidance. In sharp contrast, Chinese platforms (Douyin/Bilibili), while rich in theoretical mechanisms, exhibit a systemic deficiency in behavioral directives. This dichotomy likely reflects divergent medical communication cultures: the Western emphasis on\u0026nbsp; Enhanced Recovery After Surgery (ERAS) and shared decision-making encourages patient participation,[31, 32]\u0026nbsp;whereas Chinese content remains rooted in a traditional, didactic ‘lecture-style’ approach. Furthermore, the decoupling of user engagement from information quality—particularly evident on TikTok—reaffirms the ‘Popularity-Quality Paradox’,[33]\u0026nbsp;alerting stakeholders that algorithmic virality is a poor proxy for accuracy of online \u0026nbsp;medical information.\u003c/p\u003e\n\u003cp\u003eCharacterization of Video Attributes and Viewer Engagement Patterns\u003c/p\u003e\n\u003cp\u003eThe analysis of video attributes underscores significant divergences across digital ecosystems. Bilibili videos featured the longest median duration (180 seconds), consistent with its positioning as a ‘pan-knowledge community’ where creators favor exhaustive mechanistic explanations. However, according to the Cognitive Load Theory in multimedia learning, excessively long videos may exceed the information processing capacity of anxious patients.[34, 35]\u0026nbsp;Conversely, the brevity of videos on TikTok and Douyin (approximately 60–70 seconds) aligns with fragmented consumption habits but risks oversimplifying complex medical information. More importantly, highly compressed video formats tend to prioritize concise expert-driven explanations over narrative or experiential storytelling, which may inadvertently constrain the representation of patient-centered perspectives. Within this context, we observed a conspicuous paucity of the ‘Patient Perspective’ across all platforms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnlike chronic conditions such as diabetes where illness narratives are central to community building,[36]\u0026nbsp;anesthesia is often perceived by patients as a transient, procedure-oriented service rather than a disease requiring long-term self-management. We hypothesize this is partly attributable to the ‘pharmacological unconsciousness’ inherent to general anesthesia;[37]\u0026nbsp;patients lack subjective memory of the intraoperative period, precluding the formation of personal narratives. Consequently, the discourse power of anesthesia education remains predominantly professional-led, potentially limiting the emotional resonance required to effectively alleviate preoperative anxiety. This highlights a critical gap in current digital health communication strategies, suggesting that future perioperative educational content should deliberately integrate patient-oriented narratives to balance scientific accuracy with psychological reassurance.\u003c/p\u003e\n\u003cp\u003eVideo Ratings and Quality Evaluation\u003c/p\u003e\n\u003cp\u003eIn terms of quality assessment, YouTube demonstrated superior performance across both GQS and mDISCERN metrics, indicating greater informational depth and reliability. This advantage may be attributed less to geographic context and more to platform architecture: ecosystems that support longer-form content allow creators to provide more structured explanations, cite evidence, and disclose sources, all of which align with established standards for transparent medical communication.[38] By contrast, short-form video environments, regardless of region, may inherently constrain informational completeness. The reduced duration and algorithm-driven emphasis on engagement can encourage concise delivery at the expense of contextual nuance, citation transparency, and balanced discussion of uncertainties, potentially explaining lower reliability scores observed on other platforms. Importantly, differences were also evident across utility-related measures. According to the design philosophy of the PEMAT tool, educational materials that explicitly guide patient actions are more likely to enhance self-efficacy and informed participation.[29]\u0026nbsp;Videos emphasizing conceptual explanations without translating knowledge into actionable steps may therefore achieve reasonable informational quality while remaining limited in practical usefulness for patients facing imminent surgery.\u003c/p\u003e\n\u003cp\u003eThese findings carry important implications for both content creators and digital health strategies. For medical professionals and institutional uploaders, high-quality perioperative education should not only prioritize accuracy but also incorporate clear action-oriented guidance tailored to patient decision-making needs.[39]\u0026nbsp;For platform designers and public health communicators, the results highlight a structural tension between engagement-driven content ecosystems and the requirements of effective patient education.[40]\u0026nbsp;If unresolved, this mismatch may perpetuate information environments in which patients are informed but insufficiently empowered, ultimately limiting the capacity of digital media to reduce preoperative uncertainty and anxiety.\u003c/p\u003e\n\u003cp\u003eCorrelation Analysis and Model Prediction\u003c/p\u003e\n\u003cp\u003eCorrelation analysis further substantiated the phenomenon of ‘misplaced trust’ on social media. While engagement metrics (likes, comments) showed strong internal consistency, they were uncoupled from professional quality standards (GQS/mDISCERN). This confirms that public endorsement is driven more by entertainment value or emotional resonance than by scientific rigor, posing a significant challenge in the context of the current ‘infodemic’.[23] Regression models provided critical insights for quality improvement. Understandability (PEMAT-U) emerged as the most potent predictor of overall quality (GQS), implying that regardless of professional depth, content is perceived as high-quality only when it is accessible. Intriguingly, video duration emerged as the strongest positive predictor of reliability. This finding forcefully refutes the ‘shorter is better’ dogma prevalent in the short-video era. For a discipline involving complex pharmacology and physiological risks, micro-videos (e.g., \u0026lt;60 seconds) often fail to convey a complete chain of scientific evidence and necessary risk disclosures.[41] This suggests a fundamental conflict between algorithmic preferences and medical pedagogy.\u003c/p\u003e\n\u003cp\u003eAddressing this mismatch will likely require coordinated strategies at multiple levels. For content creators and medical institutions, designing modular educational formats, combining concise, engaging videos with links to longer evidence-based explanations, may help reconcile accessibility with informational integrity.[42] At the platform and policy level, greater emphasis on quality-sensitive recommendation mechanisms and credible source amplification could mitigate the dominance of purely engagement-driven metrics, thereby fostering digital health ecosystems that are both attention-compatible and educationally effective.\u003c/p\u003e\n\u003cp\u003eRecommendations Based on Our Results\u003c/p\u003e\n\u003cp\u003eIn light of the cross-cultural disparities and quality predictors identified in this study, we propose a strategic realignment for future anesthesia education: Content creators may improve patient utility by incorporating clearer actionable guidance alongside educational information.[43]\u0026nbsp;Clinicians may support patients by curating and recommending high-quality online resources rather than providing generic online search advice.[44]\u0026nbsp;Platform administrators and regulatory bodies may consider integrating quality-sensitive mechanisms and professional oversight to reduce the dissemination of misinformation. In addition, as increasing numbers of individuals seek health information from large language model–based systems, future evaluations should extend beyond social media videos to include AI-generated health information.[45]\u003c/p\u003e\n\u003cp\u003eThis study carries significant clinical implications. Accurate and actionable preoperative video education has been proven to reduce anxiety and improve compliance.[46] However, the internet remains a double-edged sword; high-quality digital resources can optimize the physician-patient relationship, while misinformation may precipitate unnecessary conflict.[17] By identifying structural deficits in current educational content, anesthesiologists can develop targeted supplementary materials to fill online information gaps. This approach optimizes perioperative communication efficiency and improves pain management outcomes,[6] ultimately contributing to the realization of the patient-centered Perioperative Surgical Home.[47]\u003c/p\u003e\n\u003cp\u003eLimitations\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, as a cross-sectional analysis, the data represent a temporal snapshot and may not capture the dynamic evolution of video content and algorithm-driven visibility over time.[26]\u0026nbsp;Second, the restriction to English and Chinese content limits the generalizability of findings to other linguistic or digital ecosystems that may differ in content conventions and user engagement patterns. Third, although a standardized keyword-based search strategy was employed, this approach may not fully reflect the passive information reception experience of real users within algorithmic recommendation feeds.[40]\u0026nbsp;Finally, despite the use of independent dual raters, the PEMAT and mDISCERN tools remain semi-subjective, and ratings may still be influenced by cultural or contextual perspectives.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides the first cross-cultural, multidimensional quality assessment of anesthesia-related videos on Bilibili, Douyin, TikTok, and YouTube. Results indicate that the overall quality of current content is merely moderate, with significant disparities across digital ecosystems. Our core finding unveils a profound utility gap: international platforms prioritize actionable perioperative behavioral guidance, whereas Chinese platforms are rich in theory but deficient in practical patient instructions. Furthermore, the ‘Popularity-Quality Paradox’ is particularly pronounced on algorithm-driven short-video platforms.\u003c/p\u003e\n\u003cp\u003eFuture anesthesia education requires a paradigm shift. Creators must evolve from mechanism explanation to behavioral intervention by integrating actionable checklists to enhance clinical utility. Platform regulators should introduce quality-based algorithmic weighting to sanitize the information ecology. Crucially, patients must adopt a multi-source verification strategy and avoid blind trust in high-engagement content. Only through such multi-stakeholder collaboration can we bridge the cognitive gap and achieve true information empowerment and patient safety in the perioperative period.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGQS: Global Quality Score.\u003c/p\u003e\n\u003cp\u003eCMP: Certified Medical Professionals.\u003c/p\u003e\n\u003cp\u003eOHI: Official Health Institutions.\u003c/p\u003e\n\u003cp\u003eSC: Science Communicators.\u003c/p\u003e\n\u003cp\u003ePC: Patients and Caregivers.\u003c/p\u003e\n\u003cp\u003emDISCERN: modified DISCERN.\u003c/p\u003e\n\u003cp\u003ePEMAT: Patient Education Materials Assessment Tool.\u003c/p\u003e\n\u003cp\u003ePEMAT-U: Patient Education Materials Assessment Tool–Understandability.\u003c/p\u003e\n\u003cp\u003ePEMAT-A: Patient Education Materials Assessment Tool–Actionability.\u003c/p\u003e\n\u003cp\u003eERAS: Enhanced Recovery After Surgery.\u003c/p\u003e\n\u003cp\u003eIQR: Interquartile Range.\u003c/p\u003e\n\u003cp\u003eVIF: Variance Inflation Factor.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: This study used publicly available online video data and did not involve clinical records, human biological samples, or direct interaction with human participants. Therefore, ethics approval and informed consent were not required.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: All data generated or analyzed during this study are included in this published paper and its multimedia appendices (Multimedia Appendix 1). The raw video and comment data analyzed are publicly available on the Bilibili, Douyin, YouTube, and TikTok, subject to their terms of service and content availability.\u003c/p\u003e\n\u003cp\u003eCompeting Interest: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding: This study was funded by the National Natural Science Foundation of China (72574153 to PL, 72204174 to PL), the China Postdoctoral Science Foundation (2022M722262 to PL), the Postdoctoral Program of Sichuan University (2024SCU12026 to PL), the Postdoctoral Program of West China Hospital, Sichuan University (2023HXBH009 to PL), the 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC21008 to TZ), the Sichuan Province Natural Science Foundation of China (2023NSFSC0512 to TZ), and the CAMS Innovation Fund for Medical Sciences (2023-I2M-C\u0026amp;T-B-122 to TZ).\u003c/p\u003e\n\u003cp\u003eAuthors’ Contributions:JC and GW conceived and designed the study. JC and GW were responsible for reviewing and scoring the videos. JC and GW collected and analyzed the data. GW prepared all tables and JC prepared all figures. JC, GW, and TZ wrote the original draft. PL, XH, and TZ reviewed the manuscript and provided critical revisions to the intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements: None\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMeara JG, Leather AJM, Hagander L, Alkire BC, Alonso N, Ameh EA, et al. Global surgery 2030: Evidence and solutions for achieving health, welfare, and economic development. Lancet. 2015;386:569\u0026ndash;624. https://doi.org/10.1016/S0140-6736(15)60160-X.\u003c/li\u003e\n\u003cli\u003eStamenkovic DM, Rancic NK, Latas MB, Neskovic V, Rondovic GM, Wu JD, et al. Preoperative anxiety and implications on postoperative recovery: What can we do to change our history. 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J Anesth. 2013;27:104\u0026ndash;8. https://doi.org/10.1007/s00540-012-1460-0.\u003c/li\u003e\n\u003cli\u003eIp HYV, Abrishami A, Peng PWH, Wong J, Chung F. Predictors of postoperative pain and analgesic consumption: A qualitative systematic review. Anesthesiology. 2009;111:657\u0026ndash;77. https://doi.org/10.1097/ALN.0b013e3181aae87a.\u003c/li\u003e\n\u003cli\u003eNagase K, Ando-Nagase K. Preoperative anxiety and intraoperative anesthetic requirements. Anesth Analg. 2000;91:250. https://doi.org/10.1097/00000539-200007000-00062.\u003c/li\u003e\n\u003cli\u003eLaufenberg-Feldmann R, Kappis B. Assessing preoperative anxiety using a questionnaire and clinical rating: A prospective observational study. Eur J Anaesthesiol. 2013;30:758\u0026ndash;63. https://doi.org/10.1097/EJA.0b013e3283631751.\u003c/li\u003e\n\u003cli\u003eHobson JA, Slade P, Wrench IJ, Power L. Preoperative anxiety and postoperative satisfaction in women undergoing elective caesarean section. 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Ann Behav Med. 2009;38:18\u0026ndash;27. https://doi.org/10.1007/s12160-009-9133-4.\u003c/li\u003e\n\u003cli\u003eHuo B, Boyle A, Marfo N, Tangamornsuksan W, Steen JP, McKechnie T, et al. Large language models for chatbot health advice studies: A systematic review. JAMA Netw Open. 2025;8:e2457879. https://doi.org/10.1001/jamanetworkopen.2024.57879.\u003c/li\u003e\n\u003cli\u003eRyan RE, Prictor MJ, McLaughlin KJ, Hill SJ. Audio-visual presentation of information for informed consent for participation in clinical trials. Cochrane Database Syst Rev. 2008;:CD003717. https://doi.org/10.1002/14651858.CD003717.pub2.\u003c/li\u003e\n\u003cli\u003eKwon MA. Perioperative surgical home: A new scope for future anesthesiology. Korean J Anesthesiol. 2018;71:175\u0026ndash;81. https://doi.org/10.4097/kja.d.18.27182.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anesthesia, Social Media, Patient Education, Health Informatics, Short Video","lastPublishedDoi":"10.21203/rs.3.rs-9635247/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9635247/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: As patients increasingly rely on social media for health information, the quality, reliability, and patient-oriented utility of medical video content across digital platforms remain insufficiently understood.\u003c/p\u003e\n\u003cp\u003eObjective: To evaluate and compare the quality, reliability, understandability, and actionability of anesthesia-related educational videos across major social media platforms and to identify factors associated with higher informational quality and reliability.\u003c/p\u003e\n\u003cp\u003eMethods: This cross-sectional study analyzed anesthesia-related videos collected from 4 global social media platforms (Bilibili, Douyin, TikTok, and YouTube). Using bilingual keyword searches, the top-ranked 100 videos from each platform were retrieved. Videos were independently evaluated using standardized assessment tools, and primary outcomes included overall quality (Global Quality Score [GQS]), reliability (modified DISCERN [mDISCERN]), understandability and actionability (Patient Education Materials Assessment Tool [PEMAT]). Multivariable regression analyses were conducted to identify predictors of quality and reliability.\u003c/p\u003e\n\u003cp\u003eResults: Among 400 videos analyzed, overall quality and reliability were moderate, with significant differences across platforms (P≤.002). YouTube videos demonstrated higher reliability and quality scores compared with other platforms, whereas TikTok videos showed lower quality despite higher user engagement. International platforms exhibited higher actionability (median PEMAT-A, 100%) compared with Chinese platforms (Douyin: 0%; Bilibili: 25%; P\u0026lt;.001). Longer video duration and higher understandability were positively associated with both quality and reliability. In multivariable models, understandability was the strongest predictor of overall quality (β=0.474), while video duration was the strongest predictor of reliability (β=0.434).\u003c/p\u003e\n\u003cp\u003eConclusions:\u003cstrong\u003e \u003c/strong\u003eIn this cross-sectional analysis, anesthesia-related educational videos on social media demonstrated moderate overall quality with substantial variation in patient-oriented utility across platforms. Higher understandability and longer duration were associated with improved quality and reliability. These findings highlight opportunities for improving digital patient education through clearer communication and more actionable content.\u003c/p\u003e","manuscriptTitle":"Quality, Reliability, and Patient Educational Utility of Anesthesia-Related Videos Across Global Social Media Platforms: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 10:55:47","doi":"10.21203/rs.3.rs-9635247/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b93891d0-6157-4a77-b997-2b7abcef8966","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-08T12:57:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-08T10:15:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-08T10:15:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-05-06T22:10:04+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T10:58:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 10:55:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9635247","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9635247","identity":"rs-9635247","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}