Understanding the Feline Grimace Scale: A study of dimensional structure, importance of each action unit and variables affecting assessment
Bibliographic record
Abstract
The Feline Grimace Scale (FGS) is a facial expression-based scoring system for acute pain assessment in cats. This study aimed to investigate the dimensionality, importance of each action unit (AU), and variables affecting pain assessment using the FGS. One hundred images of cat faces were scored using the FGS by five veterinarians and five veterinary students. Cats were classified as painful or pain-free, whether the cut-off for analgesia was reached during real-time assessment. Scale dimensionality was studied using principal component analysis and Horn's parallel analysis. Item-total correlation investigated correlations between each AU and total FGS scores. Linear mixed models assessed responsiveness for each AU and variables influencing scores (age, gender, pain, and group). AUs had loading values ≥ 0.6, demonstrating an association for each AU with the first principal component of the PCA. All AUs and the FGS total ratio scores were increased in painful versus pain-free cats (p < 0.001). Female raters gave higher FGS scores than male raters (p = 0.02). Muzzle tension was the only AU with sensitivity below 70 %, whereas whiskers change was the only AU with specificity below 70 %. Similarly, whiskers change and muzzle tension had the lowest area under the curve values and Youden index. The FGS is a unidimensional scale, with total scores influenced by the rater's gender and pain. FGS demonstrated strong consistency and a high correlation between the AUs and total scores. However, muzzle tension and whiskers change are less discriminative than other AUs.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".