Determinants of Performance on the Transfer Task of the Basic Laparoscopic Urologic Surgery (BLUS <sup>©</sup> ) Curriculum Administered at Objective Structured Clinical Examinations
Bibliographic record
Abstract
PURPOSE: To assess determinants of performance on the Transfer Task of the Basic Laparoscopic Urologic Surgery (BLUS(©)) skills curriculum administered at Objective Structured Clinical Examinations (OSCEs). METHODS: After obtaining Institutional Review Board approval and informed consent, urology trainees (Postgraduate Year [PGY]-3 to PGY-5) from four different training programs (A, B, C, D) were recruited for the study. Transfer Task Times (TTTs) were compared and correlated with previous laparoscopic experience, amount of endotrainer practice and scores obtained at practice sessions and other OSCE stations. RESULTS: A total of 37 trainees were evaluated on three successive semiannual OSCEs from May 2011 to May 2012, including 16 (43.2%) trainees from program A with a dedicated laparoscopic skills training program. Compared with trainees from programs B, C, and D, trainees from program A had significantly more practice per week (0 v 45 minutes, p=0.001) and significantly lower median TTTs at OSCEs (114 [68-209] v 74 [52-189] seconds, p=0.001) despite significantly lower number of laparoscopic cases assisted within the previous 6 months (13 [0-57] v 2 [0-35], p=0.001). For program A trainees, TTTs moderately correlated with median TTTs at practice sessions (r=0.57, p=0.001) and negatively correlated with amount of practice per week (r=-0.41, p=0.003). Thus, more training resulted in faster times at OSCEs. On multivariate analysis, amount of practice per week was the only significant predictor of TTTs at OSCEs (p=0.028). CONCLUSION: Performance on the transfer task of BLUS during OSCEs significantly correlated with the amount of practice rather than the number of laparoscopic cases assisted.
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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 | 0.008 |
| 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 | 0.001 |
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".