Screening criteria for increased susceptibility to heat stress during work or leisure in hot environments in healthy individuals aged 31–70 years
Bibliographic record
Abstract
Population aging and global warming generate important public health risks, as older adults have increased susceptibility to heat stress (SHS). We defined and validated sex-specific screening criteria for SHS during work and leisure activities in hot environments in individuals aged 31–70 years using age, anthropometry, and cardiorespiratory fitness. A total of 123 males and 44 females [44 ± 14 years; 22.9 ± 7.4% body fat; 40.3 ± 8.6 peak oxygen uptake (mlO2/kg/min)] participated, separated into the Analysis (n = 111) and Validation (n = 56) groups. Within these groups, participants were categorized into YOUNG (19–30 years; n = 47) and OLDER (31–70 years; n = 120). All participants performed exercise in the heat inside a direct calorimeter. Screening criteria for OLDER participants were defined from the Analysis group and were cross-validated in the Validation group. Results showed that 30% of OLDER individuals in the Analysis group were screened as SHS positive. A total of 274 statistically valid (p < 0.05) criteria were identified suggesting that OLDER participants were at risk for SHS when demonstrating two or more of the following (males/females): age ≥ 53.0/55.8 years; body mass index ≥29.5/25.7 kg/m2; body fat percentage ≥ 28.8/34.9; body surface area ≤2.0/1.7 m2; peak oxygen uptake ≤48.3/41.4 mlO2/kg fat free mass/min. In the Validation group, McNemar χ2 comparisons confirmed acceptable validity for the developed criteria. We conclude that the developed criteria can effectively screen individuals 31–70 years who are at risk for SHS during work and leisure activities in hot environments and can provide simple and effective means to mitigate the public health risks caused by heat exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".