Assessment of percutaneous renal access skills during Urology Objective Structured Clinical Examinations (OSCE)
Bibliographic record
Abstract
INTRODUCTION: The first objective was to assess percutaneous renal access (PCA) skills of urology postgraduate trainees (PGTs) during the Objective Structured Clinical Examinations (OSCEs). The second objective was to determine whether previous experience with percutaneous nephrolithotomy (PCNL) improved performance. METHODS: After obtaining ethics approval, we recruited PGTs from two urology programs in Quebec between postgraduate years (PGY-3 to PGY-5). Each trainee was asked to answer a short questionnaire regarding previous experience in endourologic procedures. After a 3-minute orientation on the PERC Mentor simulator (Simbionix, Cleveland, OH), each trainee was asked to perform task 4, where they had to correctly access all of the renal calyces and pop the balloons in a normal left kidney model. We collected and analyzed data from the questionnaire and the performance report generated by the simulator. RESULTS: In total, 13 PGTs participated in this study. PGTs had performed a median of 200 (range: 50-1000) cystoscopies, 50 (range: 10-125) TURBTs, 30 (range: 0-100) TURPs, 5 (range: 0-50) laser prostatectomies, and 50 (range: 2-125) ureteroscopies prior to this OSCE. PGTs with previous PCNL experience (8/13) had performed a mean of 18.6 ± 6.3 PCNLs. PGTs with previous PCNL experience performed significantly better in terms of shorter fluoroscopy time (10 ± 1.5 vs. 5.1 ± 0.7 min; p = 0.04), fewer attempts required for successful puncture of the pelvi-calyceal system (PCS) (21 ± 2.3 vs. 13 ± 1.8; p = 0.02), and had significantly lower complications in terms of fewer infundibular injury (7.4 ± 1.5 vs. 2 ± 0.4; p = 0.004) and fewer PCS perforations (11 ± 1.7 vs. 4.5 ± 1.2; p = 0.01). CONCLUSION: It is feasible to use the PERC Mentor simulator during OSCEs to assess PCA skills of urology PGTs. PGTs who had previous PCNL experience performed significantly better with fewer complications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| 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 | 0.000 | 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".