Outcome assessment in veterinary pain studies: a pain in animals workshop (PAW) perspective
Bibliographic record
Abstract
Biennially, the Pain in Animals Workshop (PAW) forum brings together scientists and clinicians to focus, across veterinary species and humans, on our shared passion of improving health through our ability to recognize and monitor pain. This collaboration has been instrumental in sharing current knowledge, identifying gaps, and aligning on the best paths forward in this challenging space. At the 2023 PAW held at the National Institutes of Health, Dr Dottie Brown delivered the inaugural Dr. Michele Sharkey PAW Lecture: “Outcome Assessment in Veterinary Pain Studies: The Yellow Brick Road Continues”. This perspectives article captures the content of that inaugural lecture and provides a reflection on how the PAW forums have been integral to the most recent wave of knowledge gain and awareness.
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.
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 | 0.154 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.012 | 0.034 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".