The Influence of Physical Characteristics in the Treatment of Exertional Hyperthermia by Cold Water Immersion
Bibliographic record
Abstract
Cold water immersion is the gold standard treatment for the immediate cooling of exertional heat stroke victims. However, current cooling guidelines for the use of cold water immersion do not consider physical characteristics, which are known to affect core temperature cooling rates when normothermic individuals are immersed in cold water. PURPOSE: To examine the relative influence of physical characteristics on rectal temperature cooling rates during cold water immersion following exercise-induced hyperthermia (as defined by rectal temperature of ≥39.5°C). METHODS: Data from 71 participants (59 males, 12 females) from 6 previously published studies performed in our laboratory were retrospectively analyzed using stepwise multiple regression to assess the relative influence of physical characteristics (height, body mass lean body mass, percent body fat, body surface area, body surface area to mass ratio and body surface area to lean body mass ratio) on rectal temperature cooling rate during cold water immersion performed on individuals rendered hyperthermic (39.5-40.0°C) during exercise in the heat. Whole-body cooling began within 5 min of end-exercise in a circulated cold water bath (2-8°C) and was continued until rectal temperature returned to 37.5°C. RESULTS: Participants physical characteristics were as follows: height; 177 ± 8 cm, body mass; 77.3 ± 13.0 kg, body surface area; 1.94 ± 0.19 m2, body surface area to mass ratio; 253.9 ± 19.2 cm2/kg and body surface area to lean body mass ratio; 310.3 ± 25.9 cm2/kg. Participants were immersed for 13.6 ± 6.2 min and cold water immersion provided a mean rectal temperature cooling rate of 0.21 ± 0.10°C/min. Lean body mass was the only physical characteristic included in the model that significantly explained some (7.4%) of the variance in rectal temperature cooling rate (Adjusted R2 = 0.074, P = 0.013). CONCLUSIONS: Within a broad range of individuals, lean body mass was the only predictor of rectal temperature cooling rate, however it only explained a small proportion of the variance. These data suggest that cold water immersion may be effective in negating the potential influence of physical characteristics on rectal temperature cooling rate during the treatment of exertional heat stroke. SUPPORT: Natural Sciences and Engineering Research Council of Canada (RGPIN-298159-2009, RGPIN-06313-2014).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".