Inter-Rater Reliability of the Feline Grimace Scale in Cats Undergoing Dental Extractions
Bibliographic record
Abstract
This study aimed to evaluate the inter-rater reliability of the Feline Grimace Scale (FGS) in cats undergoing dental extractions and the effects of the caregiver’s presence on the FGS scores. Twenty-four cats (6 ± 3.3 years old; 4.9 ± 1.7 kg) undergoing oral treatment were included in a prospective, blinded, randomized, clinical study. They underwent treatment under general anesthesia (acepromazine-hydromorphone-propofol-isoflurane-meloxicam-local anesthetic blocks) at day 1 and were discharged at day 6. Images of cat faces were captured from video recordings with or without the caregiver’s presence at 6 hours postoperatively (day 1), day 6, and before and after rescue analgesia. Images were randomized and independently evaluated by four raters using the FGS [five action units (AU): ear position, orbital tightening, muzzle tension, whiskers change and head position; score 0-2 for each]. Inter-rater reliability and the effects of the caregiver’s presence were analyzed with intraclass correlation coefficient [single measures (95% confidence interval)] and the Wilcoxon signed-rank test, respectively (p < 0.05). A total of 91 images were scored. Total FGS scores showed good inter-rater reliability [0.84 (0.77-0.89)]. Reliability for each AU was: ears [0.68 (0.55 - 0.78)], orbital tightening [0.76 (0.65 - 0.84)], muzzle [0.56 (0.43 - 0.69)], whiskers [0.64 (0.50 - 0.76)] and head position [0.74 (0.63 - 0.82)]. The FGS scores were not different with [0.075 (0-0.325)] or without [0.088 (0-0.525)] the caregivers’ presence (p = 0.12). The FGS is a reliable tool for pain assessment in cats undergoing dental extractions. The caregiver’s presence did not affect FGS scores.
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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 | 0.024 | 0.039 |
| 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 | 0.000 | 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".