Human Responses to 5 Heated Hypothermia Wrap Systems in a Cold Environment
Bibliographic record
Abstract
INTRODUCTION: We compared the effectiveness of 5 heated hypothermia wrap systems. METHODS: Physiologic and subjective responses were determined in 5 normothermic subjects (1 female) for 5 heated hypothermia wraps (with vapor barrier and chemical heat sources) during 60 min of exposure to a temperature of -22°C. The 5 systems were 1) user-assembled; 2) Doctor Down Rescue Wrap; 3) hypothermia prevention and management kit (HPMK); 4) MARSARS Hypothermia Stabilizer Bag; and 5) Wiggy's Victims Casualty Hypothermia Bag. Core and skin temperature, metabolic heat production, skin heat loss, and body net heat gain were determined. Subjective responses were also evaluated for whole body cold discomfort, overall shivering rating, overall temperature rating, and preferential ranking. RESULTS: The Doctor Down and user-assembled systems were generally more effective, with higher skin temperatures and lower metabolic heat production; they allowed less heat loss, resulting in higher net heat gain (P<0.05). HPMK had the lowest skin temperature and highest shivering heat production and scored worse than the other 4 systems for the "whole body cold discomfort" and "overall temperature" ratings (P<0.05). CONCLUSIONS: The user-assembled and Doctor Down systems were most effective, and subjects were coldest with the HPMK system. However, it is likely that any of the tested systems would be viable options for wilderness responders, and the choice would depend on considerations of cost; volume, as it relates to available space; and weight, as it relates to ability to carry or transport the system to the patient.
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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) | 0.000 | 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".