Learning knee arthrocentesis using YouTube videos
Bibliographic record
Abstract
BACKGROUND: This study aims to compare medical students' educational outcomes in performing knee arthrocentesis through searching and using YouTube videos versus traditional supervisor-led sessions. METHOD: Seventy-one medical students were randomly assigned to three groups. Group A had a traditional supervisor-led clinical session, where the supervisor demonstrated the procedure. Students in group B were provided with links to YouTube videos of knee arthrocentesis that were deemed to be of high educational quality, whereas group C searched and learned from any YouTube video that they found themselves based on the learning objectives provided. Student performance was first examined following the learning sessions, and then again after receiving feedback on the performance. RESULTS: Prior to feedback, statistically significant higher mean scores were noted for group A in the identification of an appropriate puncture site (p = 0.015), puncture site sterilization (p = 0.046), wearing sterile gloves (p < 0.001) and direction of needle insertion (p < 0.001). The overall mean scores (maximum possible score is 21) before feedback for groups A, B and C were 17.9 ± 1.9, 14.9 ± 2.0 and 15.4 ± 1.8, respectively (p < 0.001). The overall mean scores after feedback for groups A, B and C were 21.0 ± 0.0, 20.9 ± 0.3 and 21.0 ± 0.0, respectively (p = 0.037). CONCLUSION: Students performed equally whether they were provided with videos or found their own; however, without appropriate learner feedback from an instructor, YouTube videos cannot replace traditional supervisor-led sessions for learning knee arthrocentesis.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".