Warming by immersion or exercise affects initial cooling rate during subsequent cold water immersion.
Bibliographic record
Abstract
INTRODUCTION: We examined the effect of prior heating, by exercise and warm-water immersion, on core cooling rates in individuals rendered mildly hypothermic by immersion in cold water. METHODS: There were seven male subjects who were randomly assigned to one of three groups: 1) seated rest for 15 min (control); 2) cycling ergometry for 15 min at 70% Vo2 peak (active warming); or 3) immersion in a circulated bath at 40 degrees C to an esophageal temperature (Tes) similar to that at the end of exercise (passive warming). Subjects were then immersed in 7 degrees C water to a Tes of 34.5 degrees C. RESULTS: Initial Tes cooling rates (initial approximately 6 min cooling) differed significantly among the treatment conditions (0.074 +/- 0.045, 0.129 +/- 0.076, and 0.348 +/- 0.117 degrees C x min(-1) for control, active, and passive warming conditions, respectively); however, secondary cooling rates (rates following initial approximately 6 min cooling to the end of immersion) were not different between treatments (average of 0.102 +/- 0.085 degrees C x min(-1)). Overall Tes cooling rates during the full immersion period differed significantly and were 0.067 +/- 0.047, 0.085 +/- 0.045, and 0.209 +/- 0.131 degrees C x min(-1) for control, active, and passive warming, respectively. DISCUSSION: These results suggest that prior warming by both active and, to a greater extent, passive warming, may predispose a person to greater heat loss and to experience a larger decline in core temperature when subsequently exposed to cold water. Thus, functional time and possibly survival time could be reduced when cold water immersion is preceded by whole-body passive warming, and to a lesser degree by active warming.
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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) | 0.002 | 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".