Protection Against Cold in Prehospital Care: Wet Clothing Removal or Addition of a Vapor Barrier
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the effect of wet clothing removal or the addition of a vapor barrier in shivering subjects exposed to a cold environment with only limited insulation available. METHODS: Volunteer subjects (n = 8) wearing wet clothing were positioned on a spineboard in a climatic chamber (-18.5°C) and subjected to an initial 20 minutes of cooling followed by 30 minutes of 4 different insulation interventions in a crossover design: 1) 1 woolen blanket; 2) vapor barrier plus 1 woolen blanket; 3) wet clothing removal plus 1 woolen blanket; or 4) 2 woolen blankets. Metabolic rate, core body temperature, skin temperature, and heart rate were continuously monitored, and cold discomfort was evaluated at 5-minute intervals. RESULTS: Wet clothing removal or the addition of a vapor barrier significantly reduced metabolic rate (mean difference ± SE; 14 ± 4.7 W/m(2)) and increased skin temperature rewarming (1.0° ± 0.2°C). Increasing the insulation rendered a similar effect. There were, however, no significant differences in core body temperature or heart rate among any of the conditions. Cold discomfort (median; interquartile range) was significantly lower with the addition of a vapor barrier (4; 2-4.75) and with 2 woolen blankets (3.5; 1.5-4) compared with 1 woolen blanket alone (5; 3.25-6). CONCLUSIONS: In protracted rescue scenarios in cold environments with only limited insulation available, wet clothing removal or the use of a vapor barrier is advocated to limit the need for shivering thermogenesis and improve the patient's condition on admission to the emergency department.
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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) | 0.000 | 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".