671 The Effect of Supplementary Simulation-Based Procedural Training: The SIMULATE Randomised Controlled Clinical and Educational Trial
Bibliographic record
Abstract
Abstract Aim To evaluate whether surgical trainees undergoing additional simulation, compared to conventional training, are able to achieve proficiency sooner with better patient outcomes. Methods This international, multicentre randomised controlled superiority trial recruited urology trainees (n=94) who had performed ≤10 ureterorenoscopy (URS) cases, as a selected index procedure, with no prior simulation experience. Recruits were randomised to simulation-based training or non-simulation-based training groups, the latter of which is the current standard of training. Training sessions were conducted for the simulation arm, utilising an expert-developed multi-modality training curriculum. The primary outcome was the number of procedures required to achieve proficiency, defined as achieving a score of ≥28 on an OSATS scale, on 3 consecutive operations, without complications. Inpatient surgical complications were also recorded. All participants were followed up for 25 procedures or over 18 months. Results A total of 1140 cases were performed by 65 participants where proficiency was achieved in 21 simulation and 18 conventional participants over a median of 8 and 9 procedures, respectively (HR: 1.41 [95% CI 0.72–2.75]). More participants reached proficiency in the simulation arm in flexible ureterorenoscopy, requiring fewer number of procedures (HR 0.89 [95% CI 0.39–2.02]). Significant differences were observed in overall comparison of OSATS scores between groups (mean difference 1.42 [95% CI 0.91–1.92]; p<0.001), with fewer total complications (15 vs 37; p=0.003) and ureteric injuries (3 vs 9; p<0.001) in the simulation group. Conclusions Simulation-based training demonstrated higher overall proficiency and fewer procedures were required to achieve proficiency in the complex form of the index procedure with surgical complications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".