Validity of the MISTELS Simulator for Laparoscopy Training in Urology
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS) consists of a series of five laparoscopic exercises performed in an endotrainer box. MISTELS has been validated for use in both training and evaluation of general surgery residents in fundamental laparoscopic skills. The purpose of this study was to demonstrate the construct validity of MISTELS for urology residents. SUBJECTS AND METHODS: Seventeen participants were evaluated during performance of the five MISTELS tasks (peg transfer, pattern cutting, ligating loop, and suturing with extracorporeal and intracorporeal knots) using the standardized scoring system, which rewards both speed and precision. Participants included 13 urology residents (PGY 1-5), 1 fellow, and 3 urologists experienced in laparoscopy. Results are expressed as median (range). The Mann-Whitney U-test was used to compare MISTELS scores for 9 novice (PGY 1-4) and 8 experienced urologists (PGY 5-attending). P < 0.05 was considered statistically significant. RESULTS: The median MISTELS total normalized score for novices was 52.3 (range 15-68.9) compared with 71.7 (range 56.3-82.9) for experienced urologists (P = 0.007). Although the experienced group achieved higher scores in all five individual tasks, statistically significant differences were demonstrated for the peg transfer and intracorporeal suture tasks only. CONCLUSION: These data provide evidence for construct validity of the MISTELS system for urology residents.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".