Using the Mouse Grimace Scale to reevaluate the efficacy of postoperative analgesics in laboratory mice.
Bibliographic record
Abstract
Postoperative pain management in animals is complicated greatly by the inability to recognize pain. As a result, the choice of analgesics and their doses has been based on extrapolation from greatly differing pain models or the use of measures with unclear relevance to pain. We recently developed the Mouse Grimace Scale (MGS), a facial-expression-based pain coding system adapted directly from scales used in nonverbal human populations. The MGS has shown to be a reliable, highly accurate measure of spontaneous pain of moderate duration, and therefore is particularly useful in the quantification of postoperative pain. In the present study, we quantified the relative intensity and duration of postoperative pain after a sham ventral ovariectomy (laparotomy) in outbred mice. In addition, we compiled dose-response data for 4 commonly used analgesics: buprenorphine, carprofen, ketoprofen, and acetaminophen. We found that postoperative pain in mice, as defined by facial grimacing, lasts for 36 to 48 h, and appears to show relative exacerbation during the early dark (active) photophase. We find that buprenorphine was highly effective in inhibiting postoperative pain-induced facial grimacing in mice at doses equal to or lower than current recommendations, that carprofen and ketoprofen are effective only at doses markedly higher than those currently recommended, and that acetaminophen was ineffective at any dose used. We suggest the revision of practices for postoperative pain management in mice in light of these findings.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".