Pulmonary vascular volume, impaired left ventricular filling and dyspnea: The MESA Lung Study
Bibliographic record
Abstract
BACKGROUND: Evaluation of impaired left ventricular (LV) filling has focused on intrinsic causes of LV dysfunction; however, pulmonary vascular changes may contribute to reduced LV filling and dyspnea. We hypothesized that lower total pulmonary vascular volume (TPVV) on computed tomography (CT) would be associated with dyspnea and decrements in LV end-diastolic volume, particularly among ever-smokers. METHODS: The Multi-Ethnic Study of Atherosclerosis recruited adults without clinical cardiovascular disease in 2000-02. In 2010-12, TPVV was ascertained as the volume of arteries and veins in the lungs detectable on non-contrast chest CT (vessels ≥1 mm diameter). Cardiac measures were assessed by magnetic resonance imaging (MRI). Dyspnea was self-reported. RESULTS: Of 2303 participants, 53% had ever smoked cigarettes. Among ever-smokers, a lower TPVV was associated with a lower LV end-diastolic volume (6.9 mL per SD TPVV), stroke volume, and cardiac output and with dyspnea (all P-values <0.001). Findings were similar among those without lung disease and those with 0-10 pack-years but were mostly non-significant among never-smokers. TPVV was associated smaller left atrial volume but not with LV ejection fraction or MRI measures of impaired LV relaxation. In a second sample of ever-smokers, a lower pulmonary microvascular blood volume on contrast-enhanced MRI was also associated with a lower LV end-diastolic volume (P-value = 0.008). CONCLUSION: Reductions in pulmonary vascular volume were associated with lower LV filling and dyspnea among ever-smokers, including those without lung disease, suggesting that smoking-related pulmonary vascular changes may contribute to symptoms and impair cardiac filling and function without evidence of impaired LV relaxation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".