Prewarming Followed by Active Warming is Superior to Passive Warming in Preventing Hypothermia for Short Procedures in Adult Rats (Rattus norvegicus) Under Isoflurane Anesthesia
Bibliographic record
Abstract
General anesthesia is a common procedure in laboratory rats; however, it impairs thermoregulation, rapidly leading to hypothermia as warm core blood is distributed to the cooler periphery. The protective strategy of prewarming before the onset of anesthesia delays hypothermia, but only for a short period. This prospective, randomized, cross-over, experimental study in adult male and female SD rats (n = 8) was designed to compare passive (fleece blanket) and active (temperature controlled heating pad) warming. Initial treatment order was randomized, with a cross-over after a minimum 5 d washout period. Both groups underwent a period of prewarming in a warming box to increase core temperature by 1% (median 0.4 °C). At completion of prewarming, general anesthesia was induced and maintained for 30 min with isoflurane carried in oxygen. Core temperature was monitored for a further 30 min after anesthesia. Active warming resulted in higher core temperatures during anesthesia. During passive warming, hypothermia occurred after approximately 30 min of anesthesia and continued into recovery. In contrast, active warming prevented hypothermia. Prewarming followed by passive warming delayed hypothermia for approximately 30min, but active warming was more effective at maintaining normothermia both during and after general anesthesia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".