Facial expressions of pain in cats: the development and validation of a Feline Grimace Scale
Bibliographic record
Abstract
Grimace scales have been used for pain assessment in different species. This study aimed to develop and validate the Feline Grimace Scale (FGS) to detect naturally-occurring acute pain. Thirty-five client-owned and twenty control cats were video-recorded undisturbed in their cages in a prospective, case-control study. Painful cats received analgesic treatment and videos were repeated one hour later. Five action units (AU) were identified: ear position, orbital tightening, muzzle tension, whiskers change and head position. Four observers independently scored (0-2 for each AU) 110 images of control and painful cats. The FGS scores were higher in painful than in control cats; a very strong correlation with another validated instrument for pain assessment in cats was observed (rho = 0.86, p < 0.001) as well as good overall inter-rater reliability [ICC = 0.89 (95% CI: 0.85-0.92)], excellent intra-rater reliability (ICC > 0.91), and excellent internal consistency (Cronbach's alpha = 0.89). The FGS detected response to analgesic treatment (scores after analgesia were lower than before) and a cut-off score was determined (total pain score > 0.39 out of 1.0). The FGS is a valid and reliable tool for acute pain assessment in cats.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".