Videos on Bilibili, TikTok, and Xiaohongshu as Sources of Medical Information for Adenoid Hypertrophy: A Cross-Sectional Content Analysis (Preprint)
Bibliographic record
Abstract
Abstract Background The clinical diagnosis rate of adenoid hypertrophy (AH) in children has increased in recent years, drawing growing attention from parents. Short-video platforms such as Bilibili, TikTok, and Xiaohongshu host a large volume of educational content on this condition. However, the quality and reliability of this information remain unclear. Objective This study aimed to evaluate the completeness, understandability, actionability, reliability, and overall quality of short videos on AH across Bilibili, TikTok, and Xiaohongshu and to explore factors associated with these quality metrics, including uploader characteristics and engagement indicators. Methods We collected 220 videos (Bilibili: n=90, 40.9%; TikTok: n=63, 28.6%; and Xiaohongshu: n=67, 30.5%) using newly registered accounts. Two independent reviewers evaluated video quality using a 6-item content completeness scale (score range 0-12), the Patient Education Materials Assessment Tool for Audiovisual Materials, the modified DISCERN instrument, and the Global Quality Scale (GQS). Interrater reliability was high (Cohen κ=0.77-0.993). Completeness assessed essential informational components of AH. As data were nonnormally distributed, results are presented as median (IQR). Cross-platform comparisons were conducted using the Kruskal-Wallis H test with post hoc Mann-Whitney U tests (with Bonferroni correction). Spearman correlation was used to explore associations between video characteristics (ie, duration and engagement metrics) and quality outcomes. Stepwise linear regression identified independent predictors of overall quality (GQS). Results Video duration differed significantly across platforms (Bilibili: median 113.5, IQR 66.5-271.5 seconds; TikTok: median 73, IQR 44-100 seconds; and Xiaohongshu: median 63, IQR 41-127.5 seconds; P <.001). Bilibili videos demonstrated higher completeness than videos on the other 2 platforms (Bilibili: median 2, IQR 1.5‐4.0; TikTok: median 1.5, IQR 0.5‐2.0; and Xiaohongshu: median 1.5, IQR 0.5-2.8; P <.001); overall differences were observed for understandability and reliability, but pairwise comparisons did not reach statistical significance after Bonferroni correction. Xiaohongshu videos showed greater actionability than TikTok videos ( P =.011). Medical professionals (n=158, 71.8%) had higher understandability than nonprofessionals (n=158, 81.8% vs n=62, 66.7%; P =.001). Video duration positively correlated with completeness (ρ=0.64, 95% CI 0.56-0.71; P <.001). Shares showed weak positive correlations with completeness and actionability. Stepwise regression identified understandability (using the Patient Education Materials Assessment Tool–Understandability) as the strongest independent predictor of overall quality (GQS), followed by actionability, video duration, and uploader type; engagement metrics and platform did not enter the final model. Conclusions The quality of AH-related videos on Chinese short-video platforms is generally suboptimal. Bilibili offers higher completeness, while Xiaohongshu excels in actionability and interactivity. Understandability is the strongest predictor of overall quality, surpassing uploader type and engagement metrics. To improve online health information, platforms should move beyond engagement-based algorithms, and health care professionals should prioritize clear, actionable content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".