Flow-related right ventricular to pulmonary arterial pressure gradients during exercise
Bibliographic record
Abstract
Aims: The assumption of equivalence between right ventricular (RV) and pulmonary arterial systolic pressure is fundamental to several assessments of RV or pulmonary vascular haemodynamic function. Our aims were to (i) determine whether systolic pressure gradients develop across the RV outflow tract in healthy adults during exercise, (ii) examine the potential correlates of such gradients, and (iii) consider the effect of such gradients on calculated indices of RV function. Methods and results: Healthy untrained and endurance-trained adult volunteers were studied using right-heart catheterization at rest and during submaximal cycle ergometry. RV and pulmonary artery (PA) pressures were simultaneously transduced, and the cardiac output was determined by thermodilution. Systolic pressures, peak and mean gradients, and indices of chamber, vascular, and valve function were analysed offline. Summary data are reported as mean ± standard deviation or median (interquartile range). No significant RV outflow tract gradients were observed at rest [mean gradient = 4 (3-5) mmHg], and the calculated effective orifice area was 3.6 ± 1.0 cm2. The increase in right ventricular systolic pressure during exercise was greater than the PA systolic pressure. Accordingly, mean gradients were developed during light exercise [8 (7-9) mmHg] and increased during moderate exercise [12 (9-14) mmHg, P < 0.001]. The magnitude of the mean gradient was linearly related to the cardiac output (r2 = 0.70, P < 0.001). Conclusions: In healthy adults without pulmonic stenosis, systolic pressure gradients develop during exercise, and the magnitude is related to the blood flow rate.
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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".