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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".