A Prioritized List of Veterinary Clinical Presentations in Dogs, Cats, and Horses to Guide Curricular Content, Design, and Assessment
Bibliographic record
Abstract
The Association of American Veterinary Medical Colleges competency-based veterinary education (CBVE) framework can be used to guide curriculum and assessment design and is intended to prepare veterinary graduates for Day One of clinical practice. However, while the framework defines curricular outcomes in terms of demonstrable competencies, it does not define the specific knowledge, skills, and attitudes required to achieve those outcomes. In some human medical curricula, prioritized lists of clinical presentations guide curricular content, design, and assessment. These lists are based, in part, on practice analysis surveys. A prioritized list of this nature does not currently exist in veterinary medicine. We surveyed 1,706 veterinarians regarding the relative frequency and importance of 274 clinical presentations to generate a prioritized list by species. Acceptable statistical power was achieved for dogs, cats, and horses. These lists can be used in conjunction with the CBVE framework to inform curricular content and assessment decisions.
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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 | 0.023 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".