Performance of behavioral assays: the Rat Grimace Scale, burrowing activity and a composite behavior score to identify visceral pain in an acute and chronic colitis model
Bibliographic record
Abstract
INTRODUCTION: The Rat Grimace Scale (RGS), a facial expression scale, quantifies the affective component of pain in rats. The RGS was developed to identify acute and inflammatory pain, and applicability in acute and chronic visceral pain is unknown. The dextran sulfate sodium (DSS) colitis model is commonly used in rats, but pain is rarely assessed, instead, disease progression is monitored with the Disease Activity Index (DAI; assessing fecal blood, stool consistency, and weight loss). OBJECTIVES: The aim of this study was to assess whether the RGS and 2 additional behavioral tools (composite behavior score [CBS] and burrowing) could identify pain in an acute and chronic DSS colitis model. METHODS: Male and female Sprague-Dawley rats were block randomized to (1) acute colitis (4 days DSS in drinking water); (2) chronic colitis (4 days DSS, 7 days water, and 3 days DSS); or (3) control (14 days water). Disease Activity Index, RGS, CBS, and burrowing assessments were performed daily. RESULTS: Rat Grimace Scale scores increased as DAI scores increased during both acute and chronic phases. Burrowing only decreased during the acute phase. By contrast, CBS scores did not increase significantly during either colitis phase. CONCLUSIONS: These data show that the RGS and burrowing did not decrease in a sustained manner during chronic phase visceral pain, and that variables assessed in the DAI are indicative of pain. This suggests that the RGS can be applied to a wider range of pain types and chronicity than originally suggested. These findings increase the application of the RGS as a pain scale and welfare improvement tool.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".