Assessment of Laparoscopic Skills in Veterinarians Using a Canine Laparoscopic Simulator
Bibliographic record
Abstract
The aim of the present study was to assess the content and construct validity of the Canine Laparoscopic Simulator (CLS). Forty-two veterinarians were assigned to experienced (n=12), control (n=15), and training (n=15) groups, which were assessed while performing four laparoscopic tasks on the CLS. The initial and final assessments of all tasks were performed blindly by two experienced surgeons using the Global Operative Assessment of Laparoscopic Skills (GOALS) and a task-specific checklist. At the end of the study, the subjects completed an anonymous survey. The experienced group performed all of the tasks faster, with higher GOALS and checklist scores than the training and control groups (p≤.001). In the second assessment, the training group reduced the time needed to complete all of the tasks and obtained significantly higher GOALS and checklist scores than the control group. The participants perceived the CLS and its training program to be positive or very positive. The CLS and its training program demonstrated content and construct validity, supporting the suitability of the simulator for training and teaching and its ability to distinguish the degree of experience in laparoscopic surgery among veterinarians. In addition, face validity showed that the veterinarians fully accepted the CLS's usefulness for learning basic laparoscopic 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 | 0.002 | 0.005 |
| 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) | 0.001 | 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".