Predictive Validity of the ProMIS Hybrid Simulator in a Urology Residency Training Program
Bibliographic record
Abstract
Objectives: We assessed the predictive validity of ProMIS hybrid laparoscopic simulator in a urology residency program. Methods Between June 2008 and December 2011, we trained 14 urology residents on ProMIS, measuring 5 basic laparoscopic tasks (peg transfer, pattern cutting, EndoLoop placement, extracorporeal suturing, and intracorporeal suturing). Then, we compared their last performance on ProMIS to their first performance on a porcine laparoscopic nephrectomy model. Two independent urologic surgeons with laparoscopic experience rated the resident performance on the porcine models, and kappa test with standardized weight function was used to assess for inter-observer bias. Non-parametric spearman correlation test was used to compare each rater’s cumulative score with the cumulative score obtained on the porcine models in order to test the predictive validity of the ProMIS simulator. Results The kappa results showed acceptable agreement between the two observers amongst all domains of the rating scale of performance except for confidence of movement and efficiency. In addition, poor predictive validity of the ProMIS simulator was demonstrated. Conclusions We could not demonstrate the predictive validity for the ProMIS hybrid simulator in our urology residency program.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".