A Lifesaving Model: Teaching Advanced Procedures on Shelter Animals in a Tertiary Care Facility
Bibliographic record
Abstract
It is estimated that there are over 5 million homeless animals in the United States. While the veterinary profession continues to evolve in advanced specialty disciplines, animal shelters in every community lack resources for basic care. Concurrently, veterinary students, interns, and residents have less opportunity for practical primary and secondary veterinary care experiences in tertiary-care institutions that focus on specialty training. The two main goals of this project were (1) to provide practical medical and animal-welfare experiences to veterinary students, interns, and residents, under faculty supervision, and (2) to care for animals with medical problems beyond a typical shelter's technical capabilities and budget. Over a two-year period, 22 animals from one humane society were treated at Colorado State University Veterinary Medical Center. Initial funding for medical expenses was provided by PetSmart Charities. All 22 animals were successfully treated and subsequently adopted. The results suggest that collaboration between a tertiary-care facility and a humane shelter can be used successfully to teach advanced procedures and to save homeless animals. The project demonstrated that linking a veterinary teaching hospital's resources to a humane shelter's needs did not financially affect either institution. It is hoped that such a program might be used as a model and be perpetuated in other communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".