A Competency-based Laparoscopic Cholecystectomy Curriculum Significantly Improves General Surgery Residents’ Operative Performance and Decreases Skill Variability
Bibliographic record
Abstract
OBJECTIVE: To demonstrate the feasibility of implementing a CBE curriculum within a general surgery residency program and to evaluate its effectiveness in improving resident skill. SUMMARY OF BACKGROUND DATA: Operative skill variability affects residents and practicing surgeons and directly impacts patient outcomes. CBE can decrease this variability by ensuring uniform skill acquisition. We implemented a CBE LC curriculum to improve resident performance and decrease skill variability. METHODS: PGY-2 residents completed the curriculum during monthly rotations starting in July 2017. Once simulator proficiency was reached, residents performed elective LCs with a select group of faculty at 3 hospitals. Performance at curriculum completion was assessed using LC simulation metrics and intraoperative operative performance rating system scores and compared to both baseline and historical controls, comprised of rising PGY-3s, using a 2-sample Wilcoxon rank-sum test. PGY-2 group's performance variability was compared with PGY-3s using Levene robust test of equality of variances; P < 0.05 was considered significant. RESULTS: Twenty-one residents each performed 17.52 ± 4.15 consecutive LCs during the monthly rotation. Resident simulated and operative performance increased significantly with dedicated training and reached that of more experienced rising PGY-3s (n = 7) but with significantly decreased variability in performance ( P = 0.04). CONCLUSIONS: Completion of a CBE rotation led to significant improvements in PGY-2 residents' LC performance that reached that of PGY-3s and decreased performance variability. These results support wider implementation of CBE in resident training.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".