The cutaneous vascular response to heat stress does not explain sex‐related differences in sudomotor activity
Bibliographic record
Abstract
Changes in skin blood flow can independently modulate changes in sweat production. These findings could explain the lower whole‐body sudomotor activity recently observed in females during exercise. We therefore examined potential sex‐differences in local cutaneous and whole‐limb vascular conductance during exercise in the heat (40°C). Eight males and eight females (follicular phase) performed three successive 30 min exercise bouts at fixed rates of metabolic heat production equal to 200, 250, and 300 W·m −2 . Local sudomotor activity (ventilated capsule) and cutaneous vascular conductance (CVC, laser‐doppler) were measured on the back, chest, and forearm, while whole‐limb vascular conductance was measured at the forearm (FVC, venous occlusion plethysmography). Despite a similar requirement for heat loss during each exercise bout, increases in local sudomotor activity were significantly lower in females at each measurement site (p≤0.001). However, these differences were paralleled by similar CVC responses on the back (43 ± 2 vs. 47 ± 5%, p=0.195), chest (52 ± 5 vs. 48 ± 4%, p=0.705), and forearm (51 ± 3 vs. 60 ± 6%, p=0.212). Furthermore, FVC did not differ between sex throughout exercise (0.09 ± 0.01 vs. 0.10 ± 0.01 L·100 mL −1 ·min −1 ·mmHg −2 , p=0.278). These results suggest that the lower sudomotor activity observed in females during exercise is not due to a different cutaneous vascular response. Supported by NSERC grant RGPIN‐298159‐2009.
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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".