Simulation training in laparoscopy using a computerized physical reality simulator.
Bibliographic record
Abstract
OBJECTIVE: To describe a new simulator, SurgicalSIM LTS, and summarize our preliminary experience with system. METHODS: LTS was evaluated in 3 studies: (1) 124 participants from 3 Canadian universities: 13 students; 30 residents, fellows, attendings from surgery; 59 gynecologists; 22 urologists were classified based on laparoscopic experience as novice, intermediate, competent, or expert. All were tested on the LTS. Seventy-four were tested on the LTS and MISTELS (McGill Inanimate System for Training and Evaluation of Laparoscopic Skills). Participants completed a satisfaction questionnaire. (2) Twenty-five international gynecologists in-training at Kiel Gynaecologic Endoscopy Center, and 15 students from the center pretested on LTS underwent voluntary additional trials and posttesting. (3) Seventeen experienced laparoscopic surgeons from 3 specialties were recruited to perform on randomly assigned simulators involving 5 commercial, computer-based systems. The surgeons practiced repetitively for 1.5 days. Efficient, error-free performance was measured and proficiency score formulas were developed. RESULTS: Study A: LTS showed a good correlation with level of experience (P=0.000) and MISTELS (0.79). Satisfaction: LTS vs MISTELS 79.9 vs 70.4 (P=0.012). Study B: Posttest scores were significantly better in all tasks for both groups, P<0.0001. Group mean scores with < or =5 trials were significantly better than with 2 or 3 trials (P<0.012, P<0.018). Study C: LTS had the highest effectiveness rating of the 5 simulators. CONCLUSIONS: A new computerized physical reality simulator can be used to assess/train laparoscopic technical skills.
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.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".