Instructional Video and Medical Student Surgical Knot-Tying Proficiency: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Many senior medical students lack simple surgical and procedural skills such as knot tying. OBJECTIVE: The aim of this study was to determine whether viewing a Web-based expert knot-tying training video, in addition to the standard third-year medical student curriculum, will result in more proficient surgical knot tying. METHODS: At the start of their obstetrics and gynecology clerkship, 45 students were videotaped tying surgical knots for 2 minutes using a board model. Two blinded female pelvic medicine and reconstructive surgery physicians evaluated proficiency with a standard checklist (score range 0-16) and anchored scoring scale (range 0-20); higher numbers represent better skill. Students were then randomized to either (1) expert video (n=26) or (2) nonvideo (n=24) groups. The video group was provided unlimited access to an expert knot-tying instructional video. At the completion of the clerkship, students were again videotaped and evaluated. RESULTS: At initial evaluation, preclerkship cumulative scores (range 0-36) on the standard checklist and anchored scale were not significantly different between the nonvideo and video groups (mean 20.3, SD 7.1 vs mean 20.2, SD 9.2, P=.90, respectively). Postclerkship scores improved in both the nonvideo and video groups (mean 28.4, SD 5.4, P<.001 and mean 28.7, SD 6.5, P=.004, respectively). Increased knot board practice was significantly correlated with higher postclerkship scores on the knot-tying task, but only in the video group (r=.47, P<.05). CONCLUSIONS: The addition of a Web-based expert instructional video to a standard curriculum, coupled with knot board practice, appears to have a positive impact on medical student knot-tying proficiency.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".