Proving the Value of Simulation in Laparoscopic Surgery
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To assess the McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS) physical laparoscopic simulator for construct and predictive validity and for its educational utility. SUMMARY BACKGROUND DATA: MISTELS is the physical simulator incorporated by the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) in their Fundamentals of Laparoscopic Surgery (FLS) program. MISTELS' metrics have been shown to have high interrater and test-retest reliability and to correlate with skill in animal surgery. METHODS: Over 200 surgeons and trainees from 5 countries were assessed using MISTELS in a series of experiments to assess the validity of the system and to evaluate whether practicing MISTELS basic skills (transferring) would result in skill acquisition transferable to complex laparoscopic tasks (suturing). RESULTS: Face validity was confirmed through questioning 44 experienced laparoscopic surgeons using global rating scales. MISTELS scores increased progressively with increasing laparoscopic experience (n = 215, P < 0.0001), and residents followed over time improved their scores (n = 24, P < 0.0001), evidence of construct validity. Results in the host institution did not differ from 5 beta sites (n = 215, external validity). MISTELS scores correlated with a highly reliable validated intraoperative rating of technical skill during laparoscopic cholecystectomy (n = 19, r = 0.81, P < 0.0004; concurrent validity). Novice laparoscopists were randomized to practice/no practice of the transfer drill for 4 weeks. Improvement in intracorporeal suturing skill was significantly related to practice but not to baseline ability, career goals, or gender (P < 0.001). CONCLUSION: MISTELS is a practical and inexpensive inanimate system developed to teach and measure technical skills in laparoscopy. This system is reliable, valid, and a useful educational tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it