Durability of Endourologic Skills: Two-Year Follow-Up Study
Bibliographic record
Abstract
PURPOSE: To assess the long-term durability of endourologic skills among urology trainees after an intensive technical skills training course. SUBJECTS AND METHODS: Seventeen urology residents participated in a 2-day ureteroscopy course at a surgical skills center. Residents performed rigid ureteroscopy and basket manipulation of a small midureteral stone. Performance was assessed immediately after the course and 1 year and 2 years after training. Residents prospectively tracked all ureteroscopic cases in which they were considered the primary surgeon (i.e., performed greater than 75% of the procedure). Performance was measured using a validated global rating score (GRS), checklist score (CLS), and time required to complete the task. RESULTS: Overall, GRS improved over the 2-year follow-up (P < 0.001), with most of the improvement occurring in the first year (P = 0.03). The CLS and time to complete the task did not change (P = 0.08 and 0.12, respectively). At the 2-year follow-up, the number of cases logged had no significant effect on performance. CONCLUSIONS: Ureteroscopy skills are retained and continue to improve 2 years after completing an intense training session that uses high-fidelity bench models. Ureteroscopic experience is important for the maintenance and development of skills, even though they appear to plateau after 1 year. This result may also reflect a ceiling effect of the assessment tools.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".