VENTRICULOARTERIAL COUPLING DURING PROLONGED EXERCISE IN ENDURANCE ATHLETES
Bibliographic record
Abstract
Previous investigations have examined the effects of prolonged strenuous exercise (PSE) on left ventricular ejection fraction (LVEF). However, the effects of PSE on ventriculoarterial coupling has not been well examined in endurance athletes. PURPOSE To examine the effects of PSE on ventriculoarterial coupling and LVEF in trained cyclists. METHODS Ten male cyclists exercised on their own racing bicycles at 70 ± 5% of VO2max until exhaustion (mean exercise time:2.7 ± 0.7 hours). Two-dimensional echocardiography was used to quantify cardiac volumes and LVEF. Ventricular elastance (Ees) was calculated by the ratio of end-systolic pressure to end-systolic volume, arterial elastance (Ea) was determined by the ratio of end-systolic pressure to stroke volume, and ventriculoarterial coupling was expressed as the Ea/Ees ratio. Measurements were taken before and during exercise. RESULTS At the onset of exercise, LVEF increased by approximately 30% due to an elevation in Ees (i.e. LV contractility), and reductions in Ea (i.e. afterload) and the Ea/Ees ratio. As exercise progressed, LVEF continued to increase due to decreases in Ea while Ees and Ea/Ees remained unchanged. CONCLUSION Favorable coupling between the ventricular and arterial systems occurred in endurance cyclists during prolonged strenuous exercise and resulted in a continual increase in LVEF.
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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".