Comparison of Blood Pressure and Vascular Health in Physically Active Late Pre- and Early Postmenopausal Females
Bibliographic record
Abstract
PURPOSE: The benefits of exercise on vascular health are inconsistent in postmenopausal females. We investigated if blood pressure and markers of vascular function differ between physically active early post- and late premenopausal females. METHODS: We performed a cross-sectional comparison of 24-h blood pressure, brachial artery flow-mediated dilation, microvascular reactivity (reactive hyperemia), carotid-femoral pulse wave velocity, and cardiac baroreflex sensitivity between physically active late premenopausal (n = 16, 48 ± 2 yr) and early postmenopausal (n = 14, 53 ± 2 yr) females. RESULTS: Physical activity level was similar between premenopausal (490 ± 214 min·wk-1) and postmenopausal (550 ± 303 min·wk-1) females (P = 0.868). Brachial artery flow-mediated dilation (pre, 4.6 ± 3.9, vs post, 4.7% ± 2.2%; P = 0.724), 24-h systolic (+5 mm Hg, 95% confidence interval [CI] = -1 to +10, P = 0.972) and diastolic (+4 mm Hg, 95% CI = -1 to +9, P = 0.655) blood pressures, total reactive hyperemia (pre, 1.2 ± 0.5, vs post, 1.0 ± 0.5 mL·mm Hg-1; P = 0.479), carotid-femoral pulse wave velocity (pre, 7.9 ± 1.7, vs post, 8.1 ± 1.8 m·s-1; P = 0.477), and cardiac baroreflex sensitivity (-8 ms·mm Hg-1, 95% CI = -20.55 to 4.62, P = 0.249) did not differ between groups. By contrast, peak reactive hyperemia (-0.36 mL·min-1⋅mm Hg-1, 95% CI = -0.87 to +0.15, P = 0.009) was lower in postmenopausal females. CONCLUSIONS: These results suggest that blood pressure and markers of vascular function do not differ between physically active late pre- and early postmenopausal females.
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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) | 0.001 | 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".