A Laparoscopic Surgical Skills Assessment Tool for Veterinarians
Bibliographic record
Abstract
Our aim in this study was to validate a test of laparoscopic surgical performance by determining the relation of scores from an objective structured assessment of technical skills performed in a canine abdominal model to experience and basic laparoscopic skills. The number of years the participants had performed rigid video-endoscopic procedures (VEP), using triangulation skills, correlated positively with both evaluators' total surgical performance scores for all three evaluation methods: global rating scale, visual analog scale (VAS) rating of overall performance, and operative component rating scale (OCRS). Experience of VEP without triangulation skills (i.e., flexible endoscopy, otoscopy) or video game experience did not correlate with surgical performance. A highly validated basic laparoscopic skills assessment (McGill University inanimate system for training and evaluation of laparoscopic skills, or MISTELS) score was strongly correlated with the VAS score for surgical performance and OCRS scores. Inter-rater reliability was high for the VAS and OCRS evaluation methods, and scores from the detailed OCRS method did not differ between evaluators. In conclusion, the surgical performance test correlated with VEP triangulation experience and basic laparoscopic skills. This type of test needs to be evaluated in a larger sample population including higher numbers of veterinary laparoscopic surgeons for further validation.
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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 | 0.003 | 0.012 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".