Face, construct, and concurrent validity of a simulation model for laparoscopic ovariectomy in standing horses
Bibliographic record
Abstract
OBJECTIVE To develop and validate a simulation model for laparoscopic ovariectomy in standing horses. DESIGN Prospective cohort study. SAMPLE 15 third-year veterinary students and 4 equine surgeons with experience in laparoscopy. PROCEDURES A simulation model that mimicked laparoscopic ovariectomy in standing horses was developed. Face validity of the model was determined with a questionnaire completed by the equine surgeons. Construct validity was determined by comparing performance scores (based on time to completion and accuracy completing various operative tasks) for simulated laparoscopic ovariectomy performed in the model for the students with scores for the equine surgeons. Concurrent validity was assessed by comparing performance scores with scores obtained with the validated McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS). RESULTS Questionnaire responses indicated that the simulation model replicated the operative experience to a high degree (face validity). Performance scores for simulated laparoscopic ovariectomy performed in the model were significantly different between the students and the equine surgeons (construct validity). Performance scores for the simulation model were significantly correlated with scores for the MISTELS (concurrent validity). CONCLUSIONS AND CLINICAL RELEVANCE Results suggested that the simulation model had face, construct, and concurrent validity, suggesting that it may be useful when training students to perform laparoscopic ovariectomy in standing horses.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".