Development and validation of a cynomolgus macaque grimace scale for acute pain assessment
Bibliographic record
Abstract
Abstract Cynomolgus macaques may undergo surgical procedures for scientific and veterinary purposes. Recognition and assessment of pain using validated tools is a necessary first step for adequately managing pain in these primates. Grimace scales are one means of assessing the occurance of acute pain using action units such as facial expressions and posture. The aim of this study was to create and validate a Cynomolgus Macaque Grimace Scale (CMGS). Cynomolgus macaques (n = 43) were video recorded before and after a surgical procedure. Images were extracted from videos at timepoints at which breakthrough pain might be expected based on analgesic pharmacokinetics. Using the CMGS images were scored by 12 observers blinded to animal identification, times, and conditions. To validate the tool, detailed behavioral analyses emphasizing changes to baseline activity ethograms were compared to grimace scores. Four action units were identified related to potential pain including orbital tightening, brow lowering, cheek tightening, and hunched posture. The CMGS tool was found to have moderate inter- (ICC average action unit mean ± SD: 0.67 ± 0.28) and good intra- (ICC single mean ± SD: 0.79 ± 0.14) observer reliability. Grimace scores increased significantly (p < 0.0001) in the first four post-operative timepoints compared to baseline, correlating with behavioral findings (rho range = 0.22–0.35, p < 0.001). An analgesic intervention threshold was determined and should be considered when providing additional pain relief. The CMGS was shown to be a reliable and valid tool; however, more research is needed to confirm external validity. This tool will be highly valuable for refining analgesic protocols and acute peri-procedural care for cynomolgus macaques.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".