Effectiveness of a Head Wash Cooling Protocol Using Non‐Refrigerated Water in Reducing Heat Stress
Bibliographic record
Abstract
Envisioning a cooling method and aiming at maximum feasibility and simplicity, we designed an experimental intervention-control study based on non-refrigerated water usage, consisting of pouring 2 l of 23.0 degrees C water simultaneously on head and hands for one minute, after every 20 min of exertion. The subjects were 11 fit male individuals between 19 and 26 yr old. Each individual participated in one control and one intervention measurement in a climatic chamber at 35 degrees C and 60% humidity (31.5 degrees C WBGT) on different days. Heart rate, rectal, esophageal, skin and external ear canal temperatures were monitored constantly. Each experiment consisted of 10 min of basal recording followed by 3 intervals of 20 min of cycling and 15 min of rest. Stabilometry and visual reaction time tests were performed before and after each resting period. A questionnaire evaluating equilibrium, concentration, alertness and tiredness was administered at the beginning and at the end of every experiment. Paired t-test analysis revealed significant improvements in subjective parameters (all p<0.05), as well as skin (p<0.05), external ear canal (p<0.01) and esophageal (p<0.05) temperatures during the rest periods. Repeated measurement analysis of variance revealed significant cooling in all the aforementioned temperatures except the esophageal temperature (p=0.28). Other parameters were not significantly different. Our findings indicate that this method has subjective and physiologic positive effects, and thus can be used as a complementary low cost method to cool subjects safely.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".