Pulmonary Vascular Volume by Quantitative CT in Dyspneic Smokers with Minor Emphysema
Bibliographic record
Abstract
Reduced lung diffusing capacity for carbon monoxide (DLCO) at rest and increased ventilation (⩒E)-carbon dioxide output (⩒CO2) during exercise are frequent findings in dyspneic smokers with largely preserved FEV1. It remains unclear whether low DLCO and high ⩒E-⩒CO2 are mere reflections of alveolar destruction (i.e. emphysema) or impaired pulmonary perfusion in non-emphysematous tissue contributes to these functional abnormalities. Sixty-four smokers (41 males, FEV1= 84 ± 13%predicted) underwent pulmonary function tests, an incremental exercise test, and quantitative chest computed tomography. Total pulmonary vascular volume (TPVV) was calculated for the entire segmented vascular tree (VIDA Vision™). Using the median % low attenuation area (-950 HU), participants were dichotomized into “Trace” or “Mild” emphysema (E), each group classified into preserved versus reduced DLCO. Within each emphysema subgroup, participants with abnormally low DLCO showed lower TPVV, higher ⩒E-⩒CO2, and exertional dyspnea than those with preserved DLCO (p < 0.05). TPVV (r = 0.34; p = 0.01), but not emphysema (r = −0.05; p = 0.67), correlated with lower DLCO after adjusting for age and height. Despite lower emphysema burden, Trace-E participants with reduced DLCO had lower TPVV, higher dyspnea, and lower peak work rate than the Mild-E with preserved DLCO (p < 0.05). Interestingly, TPVV (but not emphysema) correlated inversely with both dyspnea-work rate (r = −0.36, p = 0.004) and dyspnea-⩒E slopes (r = −0.40, p = 0.001). Reduced pulmonary vascular volume adjusted by emphysema extent is associated with low DLCO and heightened exertional ventilation in dyspneic smokers with minor emphysema. Impaired perfusion of non-emphysematous regions of the lungs has greater functional and clinical consequences than hitherto assumed in these subjects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".