Assessment of photoselective vaporization of prostate skills during Urology Objective Structured Clinical Examinations (OSCE)
Bibliographic record
Abstract
INTRODUCTION: We evaluated the use of the GreenLight Simulator (GL-SIM) (American Medical Systems, Guelph, ON) in the skill assessment of postgraduate trainees (PGTs) in photoselective vaporization of the prostate (PVP). We also sought to determine whether previous PVP experience or GL-SIM practice improved performance. METHODS: PGTs in postgraduate years (PGY-3 to PGY-5) from all 4 Quebec urology training programs were recruited during 2 annual Objective Structured Clinical Examinations (OSCEs). During a 20-minute OSCE station, PGTs were asked to perform 2 exercises: (1) identification of endoscopic landmarks and (2) a PVP of a 30-g normal prostate. Grams vaporized, global scores, and number of correct anatomical landmarks were recorded and correlated with PGY level, practice on the GL-SIM, and previous PVP experience. RESULTS: In total, 25 PGTs were recruited at each OSCE, with 13 PGTs participating in both OSCEs. When comparing scores from the first and second OSCEs, there was a significant improvement in the number of grams vaporized (2.9 vs. 4.3 g; p = 0.003) and global score (100 vs. 165; p = 0.03). There was good correlation between the number of previously performed PVPs and the global score (r = 0.4, p = 0.04). Similarly, PGTs with previous practice on the GL-SIM had significantly higher global score (100.6 vs. 162.6; p = 0.04) and grams vaporized (3.1 vs. 4.1 g; p = 0.04) when compared with those who did not practice on GL-SIM. Furthermore, there were significantly more competent PGTs among those who had previously practiced on the GL-SIM (32.7% vs. 10.2%; p = 0.009). PGY level did not significantly affect grams vaporized or global score (p > 0.05). CONCLUSION: Performance on the GL-SIM at OSCEs significantly correlated with previous practice on the GL-SIM and previous PVP experience rather than PGY level. Furthermore, there were significantly more competent PGTs among those who had previously practiced on the GL-SIM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".