Development and Validation of a Uterine Prolapse, Epidural, and Vaginal Suture Model
Bibliographic record
Abstract
Uterine prolapses sporadically present to bovine practitioners. Exposing veterinary students to this is challenging due to the inability to replicate a live animal prolapses in a teaching environment. The objective of this study was to develop a model that represents each step of the process of correcting a uterine prolapse and to perform a validation study of the model and rubric used to score performance using a skill comparison between experienced veterinarians and novices (students). The model was designed and built, and 27 students and 18 bovine veterinarians were recruited to participate in the evaluation of this model. Each participant performed each step of the model while being video recorded. Following model use, all participants completed a survey on their prior experiences and opinions of the model. Videos were viewed, and performances scored by one author using a rubric. Opinions on the model were mostly favorable in regard to use and realistic experience. There was no significant difference between the scores of veterinarians and veterinary students. However, there was an association with an excellent level of global rating scores for veterinarians while the veterinary student participants were associated with borderline satisfactory to good competency levels except for the epidural. There was a statistically significant association between the global rating scores and the check list competency levels. The lack of significant difference may be attributed to students previous experiences Based on feedback from the survey responses, the model will be used in clinical skills labs to provide experience in this area.
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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 | 0.005 | 0.005 |
| 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 | 0.001 |
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".