Does fitness level modulate the cardiovascular hemodynamic response to exercise?
Bibliographic record
Abstract
Subjects with greater aerobic fitness demonstrate better diastolic compliance at rest, but whether fitness modulates exercise cardiac compliance and cardiac filling pressures remains to be determined. On the basis of maximal oxygen consumption (VO2max), healthy male subjects were categorized into either low (LO: VO2max=43+/-6 ml.kg-1.min-1; n=3) or high (HI: VO2max=60+/-3 ml.kg-1.min-1; n=5) aerobic power. Subjects performed incremental cycle exercise to 90% Vo(2max). Right atrial (RAP) and pulmonary artery wedge (PAWP) pressures were measured, and left ventricular (LV) transmural filling pressure (TMFP=PAWP-RAP) was calculated. Cardiac output (CO) and stroke volume (SV) were determined by direct Fick, and LV end-diastolic volume (EDV) was estimated from echocardiographic fractional area change and Fick SV. There were no between-group differences for any measure at rest. At a submaximal workload of 150 W, PAWP and TMFP were higher (P<0.05) in LO compared with HI (12 vs. 8 mmHg, and 9 vs. 4 mmHg, respectively). At peak exercise, CO, SV, and EDV were lower in LO (P<0.05). RAP was not different at peak exercise, but PAWP (23 vs. 15 mmHg) and TMFP (12 vs. 6 mmHg) were higher in LO (P<0.05). Compared with less fit subjects, subjects with greater aerobic fitness demonstrated lower LV filling pressures during exercise, whereas SV and EDV were either similar (submaximal exercise) or higher (peak exercise), suggesting superior diastolic function and compliance.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".