PW 2333 Efficacy of head and torso rewarming using a human model for severe hypothermia
Bibliographic record
Abstract
Objective To evaluate the rewarming effectiveness of the same amount of heat, using a charcoal heater, donated to the head or torso while using a human model for severe hypothermia where shivering is pharmacologically inhibited in mildly hypothermic subjects. Methods Six male subjects were cooled on three different occasions each, in 8°C water, for 60 min or to a core temperature of 35°C. Shivering was inhibited by intravenous meperidine (1.5 mg/kg), administered during the last ten minutes of the cold-water immersion. Subjects then exited from the cold-water immersion and then were rewarmed for 120 min by one of the following: spontaneous rewarming only; charcoal heater on the head; or charcoal heater on the torso. Supplemental meperidine (maximum cumulative dose of 3.3 mg/kg) was administered as required during rewarming to suppress shivering. Results No significant differences were found in the afterdrop amount or core rewarming rates among the three conditions. During the last 30 min of rewarming the net heat gain was significantly higher in the Head (85.8±25.3 W) and Torso (81.5±6.3 W) conditions compared to Spontaneous condition (56.9±12 W) (p<0.05). Conclusions In our study, Head and torso warming had the same core rewarming rates in a human model for severe hypothermia where shivering was pharmacologically inhibited in mildly hypothermic subjects. In non-shivering cold subjects, head warming is a viable alternative if torso warming is contraindicated. Funding NSERC, Canada.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".