Effects of training on Feline Grimace Scale scoring for acute pain assessment in cats
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to determine the effects of training on inter-rater reliability and agreement of Feline Grimace Scale (FGS) scoring by small animal practitioners. METHODS: Seven small animal veterinarians were asked to score a total of 50 images of cats in varying degrees of pain before and after training in FGS scoring. Participant scores were compared with those of an expert rater. Inter-rater reliability was analyzed using the intraclass correlation coefficient (ICC) before and after training (ICC <0.50 = poor reliability, 0.50-0.75 = moderate reliability, 0.76-0.90 = good reliability and >0.90 = excellent reliability). The Bland-Altman method was used to analyze the limits of agreement (LoAs) and bias between participants and the expert rater. RESULTS: After training, the ICC classification improved for each action unit (ear position, orbital tightening, muzzle tension, whiskers change and head position). The inter-rater reliability for the total FGS ratio scores before and after the FGS training session was moderate (ICC = 0.75; 95% confidence interval [CI] 0.66-0.83) and good (ICC = 0.80; 95% CI 0.73-0.87), respectively. Before training, LoAs were -0.277 to 0.310 with a bias of 0.016. After training, LoAs were -0.237 to 0.255 with a bias of 0.008. The bias was low (<0.1) both before and after training and LoAs did not span the FGS analgesic threshold (0.39). CONCLUSIONS AND RELEVANCE: Training in FGS scoring improved inter-rater reliability and agreement among seven small animal veterinarians and the veterinarians' skills in pain assessment.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".