Training Surgical Residents Utilizing an Animal Shelter Fracture Program
Bibliographic record
Abstract
Working relationships between veterinary medical teaching hospitals, animal shelters, and rescue groups are one way to increase veterinary students' and residents' hands-on training. The goal of this study is to describe the use of a shelter fracture program to improve the surgical skills of surgical residents. In this program, the participating shelter and rescue organizations electronically submit cases. Following evaluation of radiographs and case approval by the orthopedic faculty, the case is scheduled for a physical evaluation. A resident takes primary surgical care together with a fourth-year student rotating through the orthopedic surgery service to ensure the proper pre-, peri-, and post-operative standard of care. All care is overseen by the orthopedic faculty. A veterinary student-run fracture foster program allows students to gain additional experience in the pre-, peri-, and post-operative care of shelter animals. The total number of shelter animals treated during a 9-year period was 373, with a mean annual case load of 41.1 cases (± 10.3). During the same time period, a total of 435 client-owned cases underwent surgical fracture treatment, with a mean annual case load of 48 cases (± 11.7). Surgical resident and student surveys show that this program contributes to their knowledge, skills, and confidence in treating fracture patients. A successful cooperative program provides advanced surgical fracture treatment of shelter animals, improving animals' quality of life as well as surgical residents' and veterinary students' skills 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".