Evaluating the educational quality of surgical YouTube® videos: A systematic review
Bibliographic record
Abstract
YouTube® is the preferred source of junior trainees to prepare for surgeries. However, its educational utility is not yet determined. The objectives of this review are to assess the educational quality of YouTube® videos and to evaluate their quality and utility to complement surgical training. MEDLINE was searched for relevant articles which were screened by two independent reviewers using strict inclusion and exclusion criteria. Of 261 articles retrieved, 29 were included of which 96.6% reported unsatisfactory educational quality. Scarcity in standardized validated methods to assess the educational utility of YouTube® videos was identified. Video metrics (e.g., likes, views) do not necessarily correlate with the educational value of YouTube® videos. The educational quality of surgical YouTube® videos is inadequate, insufficient and heterogenous primarily due to its public nature. Developing a peer review process would improve the quality of uploaded videos. Moreover, academic entities should promote the development of high-quality content on open access platforms.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".