Hypercapnia Effect on Core Cooling and Shivering Threshold During Snow Burial
Bibliographic record
Abstract
INTRODUCTION: Hypercapnia during avalanche burial may increase core temperature cooling rate by decreasing the temperature threshold for shivering or by increasing respiratory heat loss. METHODS: We studied the effect of hypercapnia on rectal core temperature (T(re)) cooling rate, respiratory heat loss, heat production, and the T(re) shivering threshold during snow burial (mean snow temperature -3.2 + 2.7 degrees C) in 11 subjects. In a 60-min hypercapnic burial subjects breathed a 5% carbon dioxide and 21% oxygen inhaled gas mixture and in a separate 60-min normocapnic burial subjects breathed ambient air. After extrication from snow burial subjects were passively rewarmed in a 15 degrees C shelter and T(re) afterdrop was measured. RESULTS: The deltaT(re) over 1 h of burial in the hypercapnic study was 1.28 +/- 0.4 degrees C and in the normocapnic study was 0.97 +/- 0.4 degrees C (P = 0.045). Minute ventilation, respiratory heat loss, total metabolic rate, and metabolic rate of the respiratory muscles were greater during the hypercapnic burial. There was no difference in shivering threshold between the hypercapnic and normocapnic conditions. Afterdrop in the hypercapnic study (0.69 +/- 0.4 degrees C at 21 +/- 8.1 min after extrication) was not different than in the normocapnic study (0.86 +/- 0.3 degrees C at 23.1 +/- 5.3 min after extrication). In both the hypercapnic and normocapnic studies afterdrop cooling rate was significantly greater during extrication than during snow burial. DISCUSSION: Hypercapnia significantly increased T(re) cooling rate by increasing respiratory heat loss but did not suppress shivering. Afterdrop may significantly contribute to hypothermia during rescue of avalanche burial victims.
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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) | 0.000 | 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) | 0.001 | 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".