Measurement properties of grimace scales for pain assessment in nonhuman mammals: a systematic review
Bibliographic record
Abstract
ABSTRACT: Facial expressions of pain have been identified in several animal species. The aim of this systematic review was to provide evidence on the measurement properties of grimace scales for pain assessment. The protocol was registered (SyRF#21-November-2019), and the study is reported according to the PRISMA guidelines. Studies reporting the development, validation, and the assessment of measurement properties of grimace scales were included. Data extraction and assessment were performed by 2 investigators, following the COnsensus-based Standards for the Selection of Health Measurement INstruments guidelines. Six categories of measurement properties were assessed: internal consistency, reliability, measurement error, criterion and construct validity, and responsiveness. Overall strength of evidence (high, moderate, and low) of each instrument was based on methodological quality, number of studies, and studies' findings. Twelve scales for 9 species were included (mice, rats, rabbits, horses, piglets, sheep or lamb, ferrets, cats, and donkeys). Considerable variability regarding their development and measurement properties was observed. The Mouse, Rat, Horse and Feline Grimace Scales exhibited high level of evidence. The Rabbit, Lamb, Piglet and Ferret Grimace Scales and Sheep Pain Facial Expression Scale exhibited moderate level of evidence. The Sheep Grimace Scale, EQUUS-FAP, and EQUUS-Donkey-FAP exhibited low level of evidence for measurement properties. Construct validity was the most reported measurement property. Reliability and other forms of validity have been understudied. This systematic review identified gaps in knowledge on the measurement properties of grimace scales. Further studies should focus on improving psychometric testing, instrument refinement, and the use of grimace scales for pain assessment in nonhuman mammals.
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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 | 0.020 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".