Baseline urologic surgical skills among medical students: Differentiating trainees
Bibliographic record
Abstract
INTRODUCTION: Urology training programs seek to identify ideal candidates with the potential to become competent urologic surgeons. It is unclear whether innate technical ability has a role in this selection process. We aimed to determine whether there are any innate differences in baseline urologic technical skills among medical students. METHODS: Second-year medical students from the University of Toronto were recruited for this study and stratified into surgical and non-surgical cohorts based on their reported career aspirations. After a pre-test questionnaire, subjects were tested on several urologic surgical skills: laparoscopy, cystoscopy and robotic surgery. Statistical analysis was performed using chi-squared test, student t-tests and Spearman's correlation where appropriate. RESULTS: A total of 29 students participated in the study and no significant baseline differences were found between cohorts with respect to demographics and prior surgical experience. For laparoscopic skills, the surgical cohort outperformed the non-surgical cohort on several exercises: Lap Beans Missed (4.9 vs. 9.3, p < 0.01), Lap Bean Rating (3.8 vs. 3.1, p = 0.01), Lap Rings Error (0.2 vs. 1.22, p < 0.01), Lap Rings Rating (3.9 vs. 2.9, p < 0.01) and LapSim Grasping Score (64.3 vs. 46.4, p = 0.01). For cystoscopic skills, there were no significant differences between cohorts on any of the performance metrics. The surgical cohort also outperformed the non-surgical cohort on all measured robotic surgery performance metrics: Task Time (50.6 vs. 76.3, p < 0.01), Task Errors (0.2 vs. 3.1, p < 0.01), and Task Score (89.5 vs. 72.6, p < 0.01). DISCUSSION: Objective innate technical ability in urological skills, particularly laparoscopy and robotics, may differ between early trainees interested in a surgical career compared to those interested in a non-surgical career. Further studies are required to illicit what impact such differences have on future performance and competence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".