Psychometric Assessment of the Rat Grimace Scale and Development of an Analgesic Intervention Score
Bibliographic record
Abstract
Our limited ability to assess spontaneous pain in rodent models of painful human conditions may be associated with a translational failure of promising analgesic compounds in to clinical use. If measurement of spontaneous pain behaviours can be used to generate an analgesic intervention score their use could expand to guide the use of analgesics, as mandated by regulatory bodies and ethical and welfare obligations. One such measure of spontaneous pain, the Rat Grimace Scale (RGS), has recently been described and shown to exhibit reliability. However, reliability of measurement scores is context and content specific, and further testing required to assess translation to a heterogenous setting (different model, raters, environment). The objectives of this study were to perform reliability testing with the Rat Grimace Scale in a heterogenous setting and generate an analgesic intervention score for its use. In a randomised, blinded study, sixteen adult female rats received one of three analgesia treatments (0.05 mg/kg buprenorphine subcutaneously, 1 mg/kg meloxicam subcutaneously, 0.2 mg/kg oral buprenorphine in jelly) peri-operatively (telemetry unit implantation surgery). Rats were video-recorded (before, 1-6 and 12 hours post-operatively) and images collected for independent scoring by three blinded raters using the RGS, and five experts based on "pain/no pain" assessment. Scores were used to calculate inter- and intra-rater reliability with an intraclass correlation coefficient and generate an analgesic intervention score with receiver operating characteristic curve analysis. The RGS scores showed very good inter- and intra-rater reliability (0.85 [0.78-0.90 95% CI] and 0.83 [0.76-0.89], respectively). An analgesic intervention threshold of greater than 0.67 was determined. These data demonstrate that the RGS is a useful tool which can be successfully employed in a heterogenous setting, and has the potential to guide analgesic intervention.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".