Cooling hyperthermic firefighters by immersing forearms and hands in 10 degrees C and 20 degrees C water.
Bibliographic record
Abstract
INTRODUCTION: Firefighters experience significant heat stress while working with heavy gear in a hot, humid environment. This study compared the cooling effectiveness of immersing the forearms and hands in 10 and 20 degrees C water. METHODS: Six men (33 +/- 10 yr; 180 +/- 4 cm; 78 +/- 9 kg; 19 +/- 5% body fat) wore firefighter 'turn-out gear' (heavy clothing and breathing apparatus weighing 27 kg) in a protocol including three 20-min exercise bouts (step test, 78 W, 40 degrees C air, 40% RH) each followed by a 20-min rest/cooling (21 degrees C air); i.e., 60 min of exercise, 60 min of cooling. Turn-out gear was removed during rest/cooling periods and subjects either rested (Control), immersed their hands in 10 or 20 degrees C water (H-10, H-20), or immersed their hands and forearms in 10 or 20 degrees C water (HF-10, HF-20). RESULTS: In 20 degrees C water, hand immersion did not reduce core temperature compared with Control; however, including forearm immersion decreased core temperature below Control values after both the second and final exercise periods (p < 0.001). In 10 degrees C water, adding forearm with hand immersion produced a lower core temperature (0.8 degrees C above baseline) than all other conditions (1.1 to 1.4 degrees C above baseline) after the final exercise period (p < 0.001). Sweat loss during Control (1458 g) was greater than all active cooling protocols (1146 g) (p < 0.001), which were not different from each other. DISCUSSION: Hand and forearm immersion in cool water is simple, reduces heat strain, and may increase work performance in a hot, humid environment. With 20 degrees C water, forearms should be immersed with the hands to be effective. At lower water temperatures, forearm and/or hand immersion will be effective, although forearm immersion will decrease core temperature further.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".