Quality and Dissemination of Uterine Fibroid Health Information on TikTok and Bilibili: Cross-Sectional Study
Bibliographic record
Abstract
Background: The rise of short-video platforms, such as TikTok (Douyin in China) and Bilibili, has significantly influenced how health information is disseminated to the public. However, the quality, reliability, and effectiveness of health-related content on these platforms, particularly regarding uterine fibroids, remain underexplored. Uterine fibroids are a common medical condition that affects a substantial proportion of women worldwide. While these platforms have become vital sources of health education, misinformation and incomplete content may undermine their efficacy. Objective: This study aims to address these gaps by evaluating the quality and dissemination effectiveness of uterine fibroid-related health information on TikTok and Bilibili. Methods: A total of 200 uterine fibroid-related videos (100 from TikTok and 100 from Bilibili) were selected through a keyword search. The videos were evaluated by 2 trained gynecological experts using the Global Quality Score (GQS) and a modified DISCERN (mDISCERN) tool. In addition, the Patient Education Materials Assessment Tool for Audio and Visual Materials was used to assess the understandability and actionability of the videos. Statistical analyses, including the Mann-Whitney U test, Spearman rank correlation, and stepwise regression analysis, were used to assess differences between platforms and identify predictors of video quality. Results: The results indicated that TikTok outperformed Bilibili in terms of user engagement metrics, such as likes, comments, shares, and followers (all P<.001). However, Bilibili videos were generally longer than those on TikTok (P<.001). The videos on both platforms demonstrated suboptimal overall quality and reliability, reflected by median GQS score of 3 (IQR 3-4) for TikTok and the median GQS score of Bilibili is 3 (IQR 2-4). The median modified DISCERN scores were also low: 2 (IQR 2-2) for TikTok and 2 (IQR 2-2) for Bilibili, with no significant differences between the 2 platforms (P=.62 for GQS; P=.18 for mDISCERN). The videos on both platforms yielded comparable median scores for Patient Education Materials Assessment Tool-Understandability (PEMAT-U) and Patient Education Materials Assessment Tool-Actionability (PEMAT-A). The median score of PEMAT-U was 77% (IQR 69%-83%) for TikTok and 77% (IQR 69%-85%) for Bilibili. The PEMAT-A yielded a median score of 67% (IQR 33%-67%) for TikTok and 67% (IQR 0-67%) for Bilibili. Videos uploaded by medical professionals on TikTok had significantly higher quality scores compared to those uploaded by nonprofessionals. A moderate positive correlation was observed between the GQS and mDISCERN scores (r=0.41, P<.01), indicating an interrelationship between quality and reliability. Stepwise regression analysis identified "completeness score," "source," and "PEMAT scores" as significant predictors of video quality. Conclusions: This study highlights the generally low quality of uterine fibroid-related health information on short-video platforms, although TikTok showed better performance in terms of engagement and quality. The involvement of medical professionals was found to enhance video quality. These findings underscore the need for improved oversight of health content on social media platforms and greater involvement of health care professionals to ensure the dissemination of accurate and reliable health information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".