Evaluating the quality and educational utility of <scp>YouTube</scp> videos in teaching human surface anatomy
Bibliographic record
Abstract
Abstract YouTube is increasingly used by medical and health science students as a supplementary learning tool. However, the quality and educational value of surface anatomy videos on YouTube remain underexplored. This study aimed to systematically evaluate the quality, reliability, and educational usefulness of YouTube videos focusing on human surface anatomy. A structured YouTube search was conducted (December 2024–January 2025), targeting the seven primary body regions with specific keywords (e.g., “surface anatomy,” “bone landmarks,” and “dermatomes”). The top 30 videos per search term were selected. Two anatomists independently assessed each video using the Anatomy Content Score (ACS), Global Quality Scale (GQS), modified DISCERN (mDISCERN), and Journal of the American Medical Association (JAMA) benchmarks. Inter‐observer agreement was evaluated via Kappa coefficient. Associations between video quality scores and YouTube metrics (view count, like ratio, interaction index) were examined using nonparametric tests. Among 1050 retrieved videos, 85 (8%) met inclusion criteria; 48 (56.5%) were classified as “useful” (ACS ≥ 13, GQS ≥ 4). Longer video duration was significantly ( p < 0.001) associated with higher usefulness, whereas view count, like ratio, and interaction index did not correlate with usefulness. ACS strongly correlated with GQS ( r s = 0.754) and both correlated moderately with mDISCERN. No significant differences in video quality were observed across body regions, search rankings, presented material type, or upload period (pre‐ vs. post‐COVID‐19). YouTube offers a moderate‐quality resource for learning surface anatomy, with approximately 60% of evaluated videos deemed useful. Popularity metrics are unreliable indicators of video educational quality, underscoring the need for peer‐reviewed, high‐quality digital resources.
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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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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 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".