Short-term aerobic training and circulatory function in women: age and hormone-replacement therapy
Bibliographic record
Abstract
The physiological basis of training responses in women, and particularly older women, is not well understood. Short-term aerobic training (STAT) was used to probe the effects of age and hormone-replacement therapy (HRT) on women's ability to rapidly change peak uptake (VO2max), plasma volume and cardiac function. A total of 39 females participated in the STAT programme: 15 younger (Y; aged 19–29 years), 12 postmenopausal women undergoing HRT and 12 non-medicating postmenopausal (PM) women (aged 60–75 years). Training consisted of ten sessions of cycling over a 2-week period, which progressed in duration from 20 to 60min and in intensity from 60–75% of maximum heart rate. Plasma volume (PV; as determined by Evan's Blue dye dilution), VO2max (cycle ergometry) and cardiac function (radionuclide ventriculography) were analysed using analysis of covariance or repeated measures ANOVA. All groups demonstrated similar increase in VO2max (Y, 13%; PM, 17%; HRT, 13%), but without a significant change in left-ventricular ejection fraction and diastolic function or volumes during supine exercise. PV expansion was observed among the Y group (7%; P<0.05) but not the PM group (2%; P>0.05) or women undergoing HRT (1%; P>0.05). Age and hormone-replacement status did not affect the magnitude of VO2max change. This study suggests that STAT improves VO2max, independent of central adaptations.
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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".