Can cat caregivers reliably assess acute pain in cats using the Feline Grimace Scale? A large bilingual global survey
Bibliographic record
Abstract
Objectives This study aimed to investigate if cat caregivers could reliably assess acute pain using the Feline Grimace Scale (FGS), and if participant demographics could affect scores. Methods An online survey in English and Spanish was advertised by International Cat Care and other platforms (March–May 2021) using convenience sampling. Eligible participants were caregivers >18 years old and non-veterinary health professionals. Participants and a group of eight veterinarians scored 10 images of cats with different levels of pain. Data were analysed using linear models and intraclass correlation coefficient (ICC; α <0.05). Interpretation of the ICC was <0.2 = poor; 0.21–0.4 = reasonable; 0.41–0.60 = moderate; 0.61–0.80 = good; and 0.81–1.0 = very good. Results A total of 3039 responses were received with 1262 completed answers from 66 countries (86%, 11.1% and 2.9% identified as female, male or other, respectively). Scores for each action unit (AU; ear position, orbital tightening, muzzle tension, whiskers change and head position) and their sum (FGS score) were not significantly different between caregivers and veterinarians, except for muzzle (caregivers 0.9 ± 0.0; veterinarians 0.7 ± 0.1; P = 0.035). The ICC single (caregivers) was 0.65, 0.69, 0.58, 0.37, 0.38 and 0.65, respectively, for AU ears, eyes, muzzle, whiskers, head and sum of scores. Demographic variables did not affect FGS scores. Conclusions and relevance Total FGS scores had good reliability when used by cat caregivers, regardless of demographic variables, showing the potential applicability of the instrument to improve feline pain management and welfare worldwide.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".