Human temperature regulation under heat stress in health, disease, and injury
Bibliographic record
Abstract
The human body constantly exchanges heat with the environment. Temperature regulation is a homeostatic feedback control system that ensures deep body temperature is maintained within narrow limits despite wide variations in environmental conditions and activity-related elevations in metabolic heat production. Extensive research has been performed to study the physiological regulation of deep body temperature. This review focuses on healthy and disordered human temperature regulation during heat stress. Central to this discussion is the notion that various morphological features, intrinsic factors, diseases, and injuries independently and interactively influence deep body temperature during exercise and/or exposure to hot ambient temperatures. The first sections review fundamental aspects of the human heat stress response, including the biophysical principles governing heat balance and the autonomic control of heat loss thermoeffectors. Next, we discuss the effects of different intrinsic factors (morphology, heat adaptation, biological sex, and age), diseases (neurological, cardiovascular, metabolic, and genetic), and injuries (spinal cord injury, deep burns, and heat stroke), with emphasis on the mechanisms by which these factors enhance or disturb the regulation of deep body temperature during heat stress. We conclude with key unanswered questions in this field of research.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".