Use of a Non-traditional University Ambulatory Practice to Teach Large Animal Medicine
Bibliographic record
Abstract
While many other veterinary schools have moved away from a traditional university-based ambulatory practice, the Ohio State University's Large Animal Practice has continued to provide a cost-effective and valuable method of preparing students for today's careers in veterinary medicine. The practice provides a full array of services to production, equine, and camelid clients, including herd health, individual animal medicine and surgery, and emergency services. Acquiring established practices from alumni has formed the client base. Four full-time veterinarians operate the clinic. While these same clinicians do some classroom teaching, their primary responsibility is devoted to the five to six fourth-year veterinary students who rotate through the clinic every two weeks. Teaching methods and objectives for these students include case discussions, homework, truck quiz books, and practice management issues. Financially, the clinic runs as a private practice, with minimal support from the college (201,000 US dollars per fiscal year) and a gross income of 676,000 US dollars per year. Thus, in a cost-effective manner, this required core ambulatory rotation provides students with a scientific learning experience that exposes them to all aspects of large animal production medicine in a real-world setting.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".