Videos on Bilibili, TikTok, and Xiaohongshu as Sources of Medical Information for Adenoid Hypertrophy: A Cross-Sectional Content Analysis (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> The clinical diagnosis rate of adenoid hypertrophy in children has been rising continuously in recent years, attracting great attention from parents. There are many popular science videos about adenoid hypertrophy on short video platforms such as Bilibili, TikTok, and Xiaohongshu. It is worth noting that the quality and reliability of the popular science content related to adenoid hypertrophy on these platforms remain unknown. </sec> <sec> <title>OBJECTIVE</title> This study aimed to evaluate the completeness, understandability, actionability, reliability, and overall quality of short videos on adenoid hypertrophy from Bilibili, TikTok, and Xiaohongshu. </sec> <sec> <title>METHODS</title> Collected 220 adenoid hypertrophy videos from three platforms,two researchers independently assessed quality using content completeness,PEMAT-A/V, modified DISCERN, and GQS. Cross-platform comparisons and Spearman’s correlation were performed. </sec> <sec> <title>RESULTS</title> Bilibili videos had the longest median duration (113.50 s) and significantly outperformed TikTok and Xiaohongshu videos in terms of completeness, understandability, and reliability (p<0.05), achieving the highest overall quality. Xiaohongshu videos showed greater interactivity and actionability than those on Bilibili and TikTok. Videos produced by medical professionals are easier to understand. Correlation analysis revealed a strong positive correlation between the video duration and completeness (ρ=0.641). Interaction indicators showed high correlations among themselves (ρ>0.8) but no significant correlation with duration (ρ≤0.2).Notably, no substantial relationship was observed between the interaction indicators and video quality. </sec> <sec> <title>CONCLUSIONS</title> The content, reliability, and quality of videos on adenoid hypertrophy across short video platforms are unsatisfactory and need improvement.Overall, Bilibili provides the highest video quality and Xiaohongshu excels in interactivity. Videos produced by medical professionals are easier to understand. Platforms should enhance oversight and optimize algorithms to promote high-quality health video content. </sec>
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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 | 0.017 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".