The SIMULATE ureteroscopy training curriculum: educational value and transfer of skills
Bibliographic record
Abstract
OBJECTIVE: Different simulation modalities may be utilised in a curricular fashion to benefit from the strengths of each training model. The aim of this study is to evaluate a novel multi-modality ureterorenoscopy (URS) simulation curriculum in terms of educational value, content validity, transfer of skills and inter-rater reliability. METHODS: This international prospective study recruited urology residents (n = 46) with ≤ 10 URS experience and no prior simulation training. Participants were guided through each phase of the expert-developed SIMULATE URS curriculum by trainers and followed-up in the operating room (OR). Video recordings were obtained during training. A post-training evaluation survey was distributed to evaluate content validity and educational value, using descriptive statistics. Performance was evaluated using the objective structured assessment of technical skills (OSATS) scale to measure improvement in scores throughout the curriculum. Pearson's correlation coefficient and Cohen's kappa tests were utilised to investigate correlation and agreement between raters. RESULTS: Participants reported gaining OR-transferrable skills (Mean: 4.33 ± 0.67) and demonstrated marked improvement in throughout the curriculum, transferred to the OR for both semi-rigid URS (p = 0.004) and flexible URS (p = 0.007). 70% of participants were successfully followed-up in the OR (n = 32). No differences were identified with the additional use of fresh frozen cadavers (p = 0.85, p = 0.90) and the URO Mentor VR simulator (p = 0.13, p = 0.22). A moderate level of correlation was noted on the video OSATS assessments, between two expert assessors (r = 0.70), but a poor agreement with the live rating. CONCLUSION: The SIMULATE URS training curriculum received high educational value from participants, who demonstrated statistically significant improvement with consecutive cases throughout the curriculum and transferability of skills to the OR in both semi-rigid and flexible URS.
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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 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".