Learning knee arthrocentesis using YouTube videos
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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 it