Ability to Perform Laparoscopic Intra- and Extracorporeal Suture Ligations in a Live Canine Ovariectomy Model after Simulation Training
Bibliographic record
Abstract
Abstract Veterinary resident training in minimally invasive surgery is currently inconsistent and depends on innate psychomotor skills. Simulation training has been shown to effectively increase basic skills, but demonstration of simulation training effects on advanced skills in the operating room is sparse. We aimed to determine if simulation-trained novice surgeons were able to perform laparoscopic suture ligation in live dogs. Three novice laparoscopic surgeons underwent a 12-session simulation training program with subsequent laparoscopic skills testing to demonstrate competency. The median skills scores of trainees and of one experienced surgeon were 417 and 472, respectively. Eighteen healthy client-owned (shelter) dogs were operated on by four surgeons: one experienced American College of Veterinary Surgeons (ACVS) diplomate, two novice ACVS residents, and one novice ACVS diplomate. Laparoscopic ovariectomy was performed with suture ligation of the ovarian pedicles. Successful surgery was defined as no evidence of ovarian vessel bleeding after transection of the pedicles. Simulation-trained novices performed successful suture-ligated ovariectomies in 11/13 dogs (85%), and the experienced surgeon in 5/5 (100%) dogs. Median total ligation time was 30 minutes (range: 17–57), which was not different among surgeons ( p = .118). Median total surgery time was 105 minutes (range: 69–156) for novices and 89 minutes (range: 65–99) for the experienced surgeon ( p = .038). Extensive simulation training including suturing may contribute toward surgery residents being able to perform complex laparoscopic procedures. These results need to be confirmed in larger numbers of trainees.
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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 | 0.000 | 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 | 0.001 |
| 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".