Pulmonary Hyperinflation and Left Ventricular Mass
Bibliographic record
Abstract
BACKGROUND: Left ventricular (LV) mass is an important predictor of heart failure and cardiovascular mortality, yet determinants of LV mass are incompletely understood. Pulmonary hyperinflation in chronic obstructive pulmonary disease (COPD) may contribute to changes in intrathoracic pressure that increase LV wall stress. We therefore hypothesized that residual lung volume in COPD would be associated with greater LV mass. METHODS AND RESULTS: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study recruited smokers 50 to 79 years of age who were free of clinical cardiovascular disease. LV mass was measured by cardiac magnetic resonance. Pulmonary function testing was performed according to guidelines. Regression models were used to adjust for age, sex, body size, blood pressure, and other cardiac risk factors. Among 119 MESA COPD Study participants, the mean age was 69±6 years, 55% were male, and 65% had COPD, mostly of mild or moderate severity. Mean LV mass was 128±34 g. Residual lung volume was independently associated with greater LV mass (7.2 g per 1-SD increase in residual volume; 95% confidence interval, 2.2-12; P=0.004) and was similar in magnitude to that of systolic blood pressure (7.6 g per 1-SD increase in systolic blood pressure; 95% confidence interval, 4.3-11; P<0.001). Similar results were observed for the ratio of LV mass to end-diastolic volume (P=0.02) and with hyperinflation measured as residual volume to total lung capacity ratio (P=0.009). CONCLUSIONS: Pulmonary hyperinflation, as measured by residual lung volume or residual lung volume to total lung capacity ratio, is associated with greater LV mass.
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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 | 0.003 |
| 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) | 0.002 | 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".