The Educational Impact of Bench Model Fidelity on the Acquisition of Technical Skill
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of bench model fidelity on the acquisition of technical skill using clinically relevant outcome measures. METHODS: Fifty junior surgery residents participated in a 1-day microsurgical training course. Participants were randomized to 1 of 3 groups: 1) high-fidelity model training (live rat vas deferens; n = 21); 2) low-fidelity model training (silicone tubing; n = 19); or 3) didactic training alone (n = 10). Following training, all participants were assessed on the high- and low-fidelity bench models. Immediate outcome measures included procedure times, blinded, expert assessment of videotaped performance using checklists and global rating scales, anastomotic patency, suture placement precision, and final product ratings. Delayed outcome measures (obtained from the live rat vas deferens 30 days following training) included anastomotic patency, presence of a sperm granuloma, and the presence of sperm on microscopy. RESULTS: Following training, checklist (P < 0.001) and global rating scores (P < 0.001) on the bench model simulators were higher among subjects who received hands-on training, irrespective of model fidelity. Immediate anastomotic patency rates of the rat vas deferens were higher with increasing model fidelity training (P = 0.048). Delayed anastomotic patency rates were higher among subjects who received bench model training, irrespective of model fidelity (P = 0.02). Rates of sperm presence on microscopy were higher among subjects who received high-fidelity model training compared with subjects who received didactic training (P = 0.039) but did not differ among subjects in the high- and low-fidelity groups. CONCLUSIONS: Surgical skills training on low-fidelity bench models appears to be as effective as high-fidelity model training for the acquisition of technical skill among novice surgeons.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".