International Multicentre Validation and Transferability of the SIMULATE Ureterorenoscopy Training Curriculum
Bibliographic record
Abstract
Aim: To evaluate the face, content, construct and transfer validity of the SIMULATE ureterorenoscopy (URS) curriculum, a novel multimodality simulation-based training programme. Method: Urological trainees, from more than 15 international centres, with less than 10 URS experience (n=46) were invited for training using the curriculum, on five separate occasions. The first cohort (n=14) were additionally trained using fresh frozen cadavers with fluoroscopy. Participants were taught and assessed by specialists, using a generic OSATS scale. A One-way ANOVA test was used to evaluate the level of progress (construct validity) throughout training. Participants were followed up at their institutions and assessed on their first case to evaluate transferability. All were invited for an evaluation survey following the training. Result: Participants rated the training highly for gaining transferrable skills (mean: 4.2/5). Significant improvement was observed in semi-rigid (p=0.0005) and flexible URS (p=0.0266) procedures, with consecutive cases throughout the curriculum and in real-time operating room performance (n=21). No differences were observed in real-time performance between the cadaveric (n=9) and non-cadaveric groups (n=12; p=0.6872). Conclusion: The SIMULATE URS curriculum revealed validity and transferability. Participants are currently being followed up for 25 real-time URS procedures in comparison to a no-simulation arm, as part of the on-going SIMULATE randomised controlled trial.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".