Effects of two training curricula on basic laparoscopic skills and surgical performance among veterinarians
Bibliographic record
Abstract
OBJECTIVE: To compare laparoscopic skills among veterinarians before and after undertaking 1 of 2 programs of simulation training. DESIGN: Evaluation study. SAMPLE POPULATION: 17 veterinarians at 1 institution. PROCEDURES: Basic skills were tested by use of the McGill inanimate system for training and evaluation of laparoscopic skills (MISTELS). Surgical performance was assessed through an objective structured assessment of technical skills (OSATS). Both tests were performed prior to and after a 12-session training program, consisting of MISTELS exercises (curriculum A) or a variety of exercises (curriculum B). RESULTS: Curriculum B led to improvement of scores obtained with both the MISTELS and the OSATS. Curriculum A did not result in higher scores obtained with the MISTELS, compared with curriculum B. Curriculum A did not lead to an improvement of scores obtained with the OSATS. Participant-perceived value of the training program was correlated positively with the improvement of scores for MISTELS suturing tasks and scores obtained with the OSATS. Time spent in clinical laparoscopic surgery and curriculum B training were both positively correlated with the post-training OSATS scores but not with post-training MISTELS scores. Conversely, simulation training time correlated with an increase in MISTELS scores but not OSATS scores. CONCLUSIONS AND CLINICAL RELEVANCE: MISTELS training resulted in significant improvement of basic laparoscopic skills but not in the assessment used for surgical performance. This may have been due to the small number of study participants, the assessment tool, or the method of training. A varied curriculum may be advantageous when training veterinarians for clinical laparoscopic practice.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".