Forced-air pre-warming prevents peri-anaesthetic hypothermia and shortens recovery in adult rats
Bibliographic record
Abstract
General anaesthesia disrupts thermoregulation in mammals, which can cause hypothermia. Decreases in core body temperature of 1℃ cause significant postoperative complications in humans, and peri-anaesthetic hypothermia in mice increases data variability, which can potentially increase animal use. In rats, the impact of different temperature management strategies on the incidence and severity of hypothermia, and the accuracy of different temperature measurement methods, is unknown. Eighteen adult male and female SD rats were block-randomized to one of three treatment groups: no-warming (NW), limited-warming (LW, heat pad during anaesthesia), and pre-warming (PW, warm air exposure before anaesthesia, followed by heat pad). Anaesthesia (isoflurane) duration was for 40 min. Core body temperature (intra-abdominal telemetric temperature capsule) was recorded during anaesthesia and recovery. During anaesthesia, rectal, skin, and tail temperatures were also recorded. In the PW group, core temperature was maintained during anaesthesia and recovery. By contrast, the NW group was hypothermic (11% temperature decrease) during anaesthesia. The LW group showed a decrease in temperature during recovery. Recovery to sternal recumbency was significantly faster in the PW (125 [70-186] s, P = 0.0003) and the LW (188 [169-420] s, P = 0.04) groups than in the NW group (525 [229-652] s). Rectal temperature underestimated core temperature (bias -0.90℃, 95% limits of agreement -0.1 to 1.9℃). Skin and tail temperatures showed wide 95% limits of agreement, spanning 6 to 15℃, respectively. The novel strategy of PW was effective at maintaining core temperature during and after anaesthesia. Rectal temperature provided an acceptable proxy for core body temperature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".