Sex differences in thermoeffector responses during exercise at fixed requirements for heat loss
Bibliographic record
Abstract
To assess potential mechanisms responsible for the lower sudomotor thermosensitivity in women during exercise, we examined sex differences in sudomotor function and skin blood flow (SkBF) during exercise performed at progressive increases in the requirement for heat loss. Eight men and eight women cycled at rates of metabolic heat production of 200, 250, and 300 W/m(2) of body surface area, with each rate being performed sequentially for 30 min. The protocol was performed in a direct calorimeter to measure evaporative heat loss (EHL) and in a thermal chamber to measure local sweat rate (LSR) (ventilated capsule), SkBF (laser-Doppler), sweat gland activation (modified iodine-paper technique), and sweat gland output (SGO) on the back, chest, and forearm. Despite a similar requirement for heat loss between the sexes, significantly lower increases in EHL and LSR were observed in women (P ≤ 0.001). Sex differences in EHL and LSR were not consistently observed during the first and second exercise periods, whereas EHL (348 ± 13 vs. 307 ± 9 W/m(2)) and LSR on the back (1.61 ± 0.07 vs. 1.20 ± 0.09 mg · min(-1) · cm(-2)), chest (1.33 ± 0.06 vs. 1.08 ± 0.09 mg · min(-1) · cm(-2)), and forearm (1.53 ± 0.07 vs. 1.20 ± 0.06 mg · min(-1) · cm(-2), men vs. women) became significantly greater in men during the last exercise period (P < 0.05). At each site, differences in LSR were solely due to a greater SGO in men, as opposed to differences in sweat gland activation. In contrast, no sex differences in SkBF were observed throughout the exercise period. The present study demonstrates that sex differences in sudomotor function are only evidenced beyond a certain requirement for heat loss, solely through differences in SGO. In contrast, the lower EHL and LSR in women are not paralleled by a lower SkBF response.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".