Agreement and reliability of the Feline Grimace Scale among cat owners, veterinarians, veterinary students and nurses
Bibliographic record
Abstract
This study aimed to evaluate the agreement and reliability of the Feline Grimace Scale (FGS) among cat owners, veterinarians, veterinary students and nurses/technicians. Raters (n = 5/group) scored 100 images using the FGS (ear position, orbital tightening, muzzle tension, whiskers position and head position). Intra-class correlation coefficients (ICC) were used to assess inter- and intra-rater reliability. Agreement between each group and the veterinarian group (gold-standard) was calculated using the Bland-Altman method. Effects of gender, age and number of cats owned on FGS scores were assessed using linear mixed models. Inter-rater reliability was good for FGS final scores (ICC > 0.8). The muzzle and whiskers yielded lower reliability (ICC = 0.39 to 0.74). Intra-rater reliability was excellent for students and veterinarians (ICC = 0.91), and good for owners and nurses (ICC = 0.87 and 0.81, respectively). A very good agreement between all groups and veterinarians (bias < 0.1 and narrow limits of agreement) was observed. Female raters assigned higher FGS scores than males (p = 0.006); however, male raters were underrepresented in this study. Scores were not affected by age or number of cats owned. The FGS is reliable for feline acute pain assessment when used by individuals with different experience.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".