2022 <scp>WSAVA</scp> guidelines for the recognition, assessment and treatment of pain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it