Assessment of Veterinary Students’ Responses to Questions Regarding Small Animal Pain Recognition and Analgesic Treatment Options
Bibliographic record
Abstract
Pain recognition and treatment in companion animals are important aspects of veterinary medicine, yet the teaching of these concepts may not be adequate at all academic institutions. This study was designed to evaluate veterinary students’ ability to recall signs of pain and specific analgesic drugs in dogs and cats. We hypothesized that students in the fourth, or final, year of their veterinary curriculum would have a better understanding of pain recognition and be able to recall more analgesic options. A brief, voluntary, and anonymous open question survey was made available to all veterinary students, years 1 to 4, at our institution. The questions included, “How does a cat/dog show signs of pain?” and “What pain medications are used in cats/dogs?” Survey responses were collated according to the students’ year in the curriculum, and the most common responses for signs of pain and analgesic medications recalled by the students in both the cat and dog were compared for significant differences. Results showed that students in the class of 2017 (seniors) had no superior recall of analgesic medications or recognition of pain in cats or dogs compared to the other classes. Vocalization was the most common sign of pain recalled with at least 50% responses from all classes. Carprofen was the most commonly recalled analgesic for dogs (the difference between classes, p = .04). Meloxicam was the most commonly recalled analgesic for cats (the difference among classes, p < .001). Based on these results, areas of improvement were identified for our analgesic curriculum.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".