Evaluation of a welfare assessment tool to examine practices for preventing, recognizing, and managing pain at companion-animal veterinary clinics.
Bibliographic record
Abstract
= 0.92) agreement, which suggests that the tool reliably collects information about pain management practices. Interviews were completed at all recruited clinics, which indicates high feasibility for the methods. Validity could not be assessed, as participants were reluctant to share information about analgesic administration from their clinical records. Descriptive results indicated areas for which many veterinarians are acting in accordance with best practices for pain management, such as pre-emptive and post-surgical analgesia for ovariohysterectomy patients, and post-surgical care instructions. Areas that offer opportunity for enhancement were also highlighted, e.g., training veterinary staff to recognize signs of pain and duration of analgesia in ovariohysterectomy patients after discharge. Overall, based on this limited sample, most veterinarians appear to be effectively managing their patients' pain, although areas with opportunity for enhancement were also identified. Further research is needed to assess trends in a broader sample of participants.
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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 | 0.040 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".