2022 <scp>WSAVA</scp> guidelines for the recognition, assessment and treatment of pain
Bibliographic record
Abstract
Abstract Animal sentience refers to the capacity of animals to feel both positive and negative emotions including that of pain. As veterinary health professionals, we have a medical and ethical duty to mitigate suffering from pain to the best of our ability. In 2014, the first Global Pain Council World Small Animal Veterinary Association (WSAVA) Guidelines for the Recognition, Assessment and Treatment of Pain was published and remains to this day one of the most relevant and widespread documents of its kind. The 2022 WSAVA Global Pain Management Guidelines evolves from the first document with updated scientific information reflecting major advances in veterinary pain medicine in the last decade. This document is designed to provide the user with easy‐to‐implement, core fundamentals on the successful recognition and treatment of pain in the day‐to‐day small animal clinical practice setting. It provides basic and practical information with an extensive reference list to guide those who want to further their knowledge on pain management. The 2022 WSAVA Global Pain Management Guidelines should be easily implemented regardless of practice setting and/or location for the promotion and advance of pain management and animal welfare.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.098 | 0.101 |
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".