Late Breaking Abstract - The effect of negative intrathoracic pressure and dynamic hyperinflation on heart-lung interaction during exercise
Bibliographic record
Abstract
Increased negative intrathoracic pressure (nITP) and dynamic hyperinflation (DH) influence left ventricular (LV) filling and stroke volume (LVSV) at rest through changes in preload, afterload, and direct ventricular interaction (DVI). Whether nITP and DH influence LVSV expansion during exercise has not been studied. We investigated the hemodynamic effects of increased nITP alone and in combination with DH (nITP+DH) during constant-load cycling exercise (CLE). Participants (mean±SD: age=25±3yr, n=20, 10F:10M) performed CLE at 60% of supine maximal workload with a) inspiratory resistance of -20 cmH2O (nITP) and b) inspiratory and expiratory resistances (nITP+DH, ΔEELV~+400ml). Echocardiography was performed at rest, at steady state and at regular intervals during DH+nITP until symptom limitation (12.7±3.0min). Subjects repeated the CLE without resistive loading (CON) with isotime echo measurements performed. During exercise, nITP had no significant effect on LV volumes or geometry but increased LV afterload measured by LV end-systolic wall stress (LVESWS, p=0.06). In contrast, nITP+DH reduced LV end diastolic volume (LVEDV; 97±25 vs 88±17ml; p=0.03) and LVSV (62±15 vs 58±12 ml, p=0.05) compared to CON, while cardiac output was unchanged due to an increased heart rate (p<0.01). Radius of septal curvature at end-diastole (11±13 vs 20±24%, p=0.003) was increased compared to CON demonstrating DVI, without increasing LVESWS (p=0.42). Our findings suggest that DH impacts LVEDV and LVSV during exercise primarily through DVI as LV afterload was unchanged. These adverse changes in LV hemodynamics with DH may contribute to exercise intolerance in patients with COPD.
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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 | 0.000 |
| 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) | 0.004 | 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".