Predictors of Technical Skill Acquisition Among Resident Trainees in a Laparoscopic Skills Education Program
Bibliographic record
Abstract
BACKGROUND: Administrative and financial pressures on surgical education have created a need for efficient training curricula. Predictors of innate technical ability, which would guide the optimization of such a curriculum, are not well described. The goal of this study was to identify student characteristics predictive of innate pretraining skill level and response to training during the course of a four-week laparoscopic skills development program. METHODS: Laparoscopic skills in 35 first-year surgical residents were assessed with the McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS) before and after a four-week skills training program and after an interval of approximately 1 year. The correlation between trainee characteristics, including age, sex, designated surgical specialty, and laparoscopic skill level was assessed by using Pearson's correlation and paired t-test studies. RESULTS: Intake MISTELS scores showed no significant correlation to age, sex, or designated field. Interns designated for the general surgery training program had significantly higher final scores than those entering other fields (p = 0.02). There was a negative correlation between trainee age and both degree of improvement during training and final scores (p = 0.02 and 0.05). A history of video game use correlated with significantly higher initial scores and better skills retention (p = 0.03 and 0.04). CONCLUSIONS: A laparoscopic technical curriculum can achieve basic proficiency even when taught to a diverse group of trainees. Older residents beginning their surgical careers may be slower to develop technical skills. Choice of subspecialty seems to predict higher level of proficiency after completion of a skills training program among resident students.
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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 | 0.004 |
| 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 | 0.001 |
| 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".