Development and Validation of a Bovine Left Displaced Abomasum Reduction Model and Rubric
Bibliographic record
Abstract
Abstract Left displaced abomasum (LDA) is a common condition in dairy cattle where the abomasum dilates and migrates to the left side of the abdomen. This condition causes significant economic losses for farmers and can result in life-threatening complications, so it is critical that veterinary students be taught to surgically correct a LDA before graduating and entering food animal practice. Models have been successfully used to teach students to perform other surgical procedures, but limited models exist to teach surgical skills to prospective dairy veterinary students. This study sought to develop and validate a bovine LDA reduction model and scoring rubric using a validity framework consisting of content evidence (expert opinion), internal structure evidence (reliability of rubric scores), and evidence showing the relationship with other variables (comparing expert to novice performance). Experienced veterinarians ( n = 12) and novice veterinary students ( n = 30) surgically deflated and reduced the model's LDA while being recorded. Videos were scored by a blinded expert. Participants completed a survey afterward. All veterinarians reported that the model was suitable for use in teaching and assessing students, offering content evidence for validation. Scores produced by the checklist had good reliability (α = .886), offering internal structure evidence. Veterinarians achieved higher checklist ( p = .025) and global rating scores ( p = .005) than students, offering relationship with other variables evidence. The development and use of food animal models promotes students’ development of competence in performing food animal procedures, leading to better qualified new graduates entering food animal practice. The use of models also protects animal welfare during students’ training.
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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 | 0.036 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".