Physiological factors characterizing heat-vulnerable older adults: A narrative review
Bibliographic record
Abstract
More frequent and intense periods of extreme heat (heatwaves) represent the most direct challenge to human health posed by climate change. Older adults are particularly vulnerable, especially those with common age-associated chronic health conditions (e.g., cardiovascular disease, hypertension, obesity, type 2 diabetes, chronic kidney disease). In parallel, the global population is aging and age-associated disease rates are on the rise. Impairments in the physiological responses tasked with maintaining homeostasis during heat exposure have long been thought to contribute to increased risk of health disorders in older adults during heatwaves. As such, a comprehensive overview of the provisional links between age-related physiological dysfunction and elevated risk of heat-related injury in older adults would be of great value to healthcare officials and policy makers concerned with protecting heat-vulnerable sectors of the population from the adverse health impacts of heatwaves. In this narrative review, we therefore summarize our current understanding of the physiological mechanisms by which aging impairs the regulation of body temperature, hemodynamic stability and hydration status. We then examine how these impairments may contribute to acute pathophysiological events common during heatwaves (e.g., heatstroke, major adverse cardiovascular events, acute kidney injury) and discuss how age-associated chronic health conditions may exacerbate those impairments. Finally, we briefly consider the importance of physiological research in the development of climate-health programs aimed at protecting heat-vulnerable individuals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".