A Comparison of Passive Rewarming Systems Following Cold Water Immersion
Bibliographic record
Abstract
INTRODUCTION: We studied field rewarming using a typical winter sleeping bag versus two heated hypothermia wrap systems in a semi-realistic lab simulation. METHODS: 10 participants (8 M, 2 F) were cooled to 36.1°C core temperature through 10.5-11.5°C water immersion, then performed 60 min of passive rewarming in 0°C air. The rewarming methods tested were: 1) a -9°C rated mummy-style Sleeping Bag; 2) Doctor Down Rescue Wrap; and 3) Thermal Yielding Vascular Airway Capsule (TYVAC) system; the latter two methods included vapor barriers and two heating pads. Rectal and skin temperatures, along with metabolic heat production calculated via indirect calorimetry, were measured throughout rewarming. RESULTS: One male participant was removed from analysis due to lack of sufficient cooling. Rectal temperature decreased in the remaining participants by ∼1.1-1.2°C to 36.1°C during the initial immersion phase. Over the 60 min of rewarming, rectal temperature changes were Δ0.0 ± 0.6°C in a sleeping bag, Δ+0.2 ± 0.3°C in Doctor Down, and Δ+0.2 ± 0.3°C in TYVAC, with no significant differences across methods. Mean skin temperatures, metabolic heat production, and perceptual measures were also similar across methods with no method×time interactions. CONCLUSIONS: After 60 min of passive rewarming in cold conditions, all three rewarming methods were able to stall continued core cooling to levels at or slightly above post-immersion temperatures. With no differences in any physiological measures, it appears that all three rewarming methods are equally viable options for wilderness responders, and the choice should come down to environmetal conditions, availability, convenience, and ergonomics rather than rewarming efficacy.
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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 | 0.002 |
| 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 | 0.000 |
| 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".