Does aging alter skin vascular function in humans when spatial variation is considered?
Bibliographic record
Abstract
OBJECTIVE: Reports evaluating age-related impairments in cutaneous vascular function assessed by either the venoarteriolar reflex (VAR) induced by venous congestion, or post-occlusive reactive hyperemia (PORH) activated by arterial occlusion, have yielded mixed findings. This may be due to region-specific variability that occurs when assessing local cutaneous vascular responses. We evaluated the hypothesis that aging attenuates VAR and PORH responses in forearm skin assessed across four adjacent sites, each separated by ~4 cm to account for inter-site variability. METHODS: In twenty young (24 ± 4 years, 10 females) and twenty older (60 ± 7 years, 9 females) adults, VAR and PORH were achieved by a 3-min venous occlusion and 5-min arterial occlusion, each induced by inflating a pressure cuff to 45 and 240 mmHg, respectively. Cutaneous blood flow at all skin sites was measured by laser-Doppler flowmetry with the average response from all sites used for between-group comparisons. RESULTS: VAR and PORH responses were similar between groups with the exception that the time required to achieve peak PORH was delayed in older adults (mean difference of 5.5 ± 4.4 s, p = 0.003, Cohen's d = 0.812). CONCLUSIONS: We showed that aging had a negligible influence on VAR and PORH responses in forearm skin even when controlling for region-specific variability.
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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 | 0.003 |
| 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) | 0.001 | 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".