Challenges using the Feline Grimace Scale in brachycephalic cats with ocular pain
Bibliographic record
Abstract
Objectives This study investigated the inter-rater reliability, agreement and responsiveness of the Feline Grimace Scale (FGS) in brachycephalic cats. Methods A total of 28 brachycephalic cats (mean age 6.6 ± 4.4 years, mean weight 4.2 ± 1.0 kg) undergoing ocular surgery were included in a prospective, randomised, blinded study. Cats presenting fear-anxiety behaviours were not enrolled. In total, 95 images of these cats were collected from video recordings pre- and postoperatively (before/after analgesia), scored by four raters using the FGS and compared with real-time scores. Limits of agreement (LoAs) and bias were evaluated using the Bland–Altman method (good or poor agreement if bias <0.1 or >0.1, respectively). Inter-rater reliability was assessed using the intraclass correlation coefficient (ICC; <0.50 = poor, 0.50–0.75 = moderate, 0.76–0.90 = good and >0.90 = excellent reliability). Generalised linear mixed models evaluated responsiveness ( P <0.05). Results Inter-rater reliability (ICC single ) was poor for muzzle tension (0.47, 95% confidence interval [CI] 0.36–0.58) and whiskers change (0.34, 95% CI 0.22–0.46), good for ear (0.81, 95% CI 0.74–0.86) and eye position (0.84, 95% CI 0.79–0.88), moderate for head position (0.71, 95% CI 0.59–0.79) and good for FGS total ratio scores (0.76, 95% CI 0.68–0.82). LoAs were in the range of –0.37 to 0.22 with a bias of –0.08, suggesting that some cats could have their scores affected in comparison with real-time scores. Mean FGS total scores decreased after analgesia pre- (0.56 ± 0.10 vs 0.38 ± 0.15; P = 0.005) and postoperatively (0.60 ± 0.18 vs 0.36 ± 0.15; P <0.001). Conclusions and relevance The FGS is a responsive pain-scoring instrument in brachycephalic cats with ocular pain, with good agreement and excellent inter-rater reliability for total ratio scores. Pain may be overestimated using image assessment in some brachycephalic 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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".