An exploratory assessment of regional cutaneous vasodilator responses to local heating in young and older females
Bibliographic record
Abstract
ABSTRACT Objective To explore regional differences and potential age effects in cutaneous vascular conductance (%CVC max ) during local heating in young and older females. Methods In 10 young (21.4 ± 3.4 years) and 10 older (69.8 ± 2.7 years) females, %CVC max was assessed using laser-Doppler flowmetry at the chest, abdomen, forearm, and thigh during rapid, local skin heating. Local temperature was set at 34°C during baseline and increased to 39°C, then 42°C in 30 min increments each. Differences in %CVC max between and within age groups were evaluated at baseline, the initial vasodilator peak, and both the 39°C and 42°C heating plateaus. Results In young females, responses were similar across regions with the exception that %CVC max was reduced for the abdomen across heating phases relative to other regions (all P < 0.040). In contrast, calf responses in older females were greater compared to other regions during the 39°C plateau only (all P < 0.049). %CVC max was greater at the abdomen during the 42°C plateau ( P = 0.022) for older females compared to young. Similarly, responses pooled across sites were significantly different ( P = 0.035). Conclusion In our exploratory study we observed regional differences for both young and older females, and the pattern of response and the heating phases where differences occurred varied. Further, no age-related differences in %CVC max were observed apart from a marginally greater response for older females at peak heating of 42°C. These findings highlight the need to disentangle the effects of sex and age in evaluating the vascular responses to heat and provide a critical foundation for sex-specific investigations into microvascular dysfunction.
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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 | 0.000 |
| 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) | 0.002 | 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".