Hypothermia During General Anesthesia Interferes with Pain Assessment in Laboratory Rats (<i>Rattus norvegicus</i>)
Bibliographic record
Abstract
Accurate pain assessment methods are necessary to ensure animal welfare and reliable data collection in animal research. The Rat Grimace Scale (RGS), a facial expression pain scale, allows effective identification of pain. However, the potential confounds of this method remain mostly unexplored. General anesthesia, which is used in many laboratory procedures, suppresses thermoregulation and results in hypothermia. We investigated the effects of isoflurane-induced hypothermia on RGS scores. Twenty (10 male and 10 female) Sprague-Dawley rats each received 30 min of anesthesia, followed by 30 min of observation after the return of sternal recumbency. Rats were randomized to receive warming with an electric heating pad or no warming during both periods. Unwarmed rats became hypothermic within 15 min after isoflurane exposure began and returned to normothermia within 15 min after returning to sternal recumbency. Warmed rats did not deviate from the normothermic range. The RGS scores of unwarmed rats were significantly higher than baseline levels for 3 h after anesthesia and were higher than those of warmed rats at 5 and 180 min after anesthesia. Hypothermia resulted in a larger proportion of rats crossing a predetermined analgesic intervention threshold. Our findings show that hypothermia induced by isoflurane anesthesia presents a confound to accurate RGS scoring. These results emphasize the importance of maintaining normothermia to avoid inflated pain scores and to obtain accurate pain assessment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".