Influence of hormone replacement therapy and aerobic exercise training on oxygen uptake kinetics in postmenopausal women
Bibliographic record
Abstract
The purpose of this study was to determine the effects of aerobic exercise training on the adjustment of pulmonary oxygen (O2) uptake (VO2p) kinetics in postmenopausal women in 2 groups: those using hormone replacement therapy (HRT) (HRT group) (n = 7, aged 56 ± 4 years) and those not using HRT (nonHRT group) (n = 8, aged 60 ± 5 years). The influence of training (cycle-ergometer 3 times per week for 6 weeks) on step transitions to both moderate-intensity (80% of the gas exchange threshold) and heavy-intensity (Δ50) cycling exercise was studied. Breath-by-breath VO2p data were collected using a mass spectrometer. There were no differences in baseline characteristics between the HRT and nonHRT groups. Moderate-intensity exercise VO2p kinetics were significantly speeded (p < 0.05) with the τVO2p decreasing from 46 ± 8 s before training to 32 ± 4 s after training. Similarly, during the heavy-intensity exercise, on-transient phase 2 τVO2p was reduced from before training (48 ± 7 s) to after training (38 ± 6 s). The use of HRT did not influence the effect of the endurance exercise training on τVO2p during moderate or heavy exercise in healthy postmenopausal women. To provide insight into the mechanism of adjustment, knee extension exercise was studied, and the VO2p kinetics were significantly speeded (p < 0.05), with the τVO2p of the knee extension exercise decreasing from 62.2 ± 18.3 s before training to 48.0 ± 16.2 s after training. Thus, 6 weeks of exercise training resulted in appreciably faster cycling phase 2 VO2p kinetics during moderate and heavy exercise in older women, independent of HRT use.
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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".