Pulmonary Artery Abnormalities in Ex-smokers with and without Airflow Obstruction
Bibliographic record
Abstract
Pulmonary vascular disease is a common complication of chronic obstructive pulmonary disease (COPD), and an important risk factor for COPD exacerbations and death. We explored the relationship between pulmonary artery volumes measured using thoracic computed tomography (CT) and lung structure-function measured using spirometry, CT and magnetic resonance imaging (MRI) in 124 ex-smokers with (n = 68) and without (n = 56) airflow obstruction, and a control group of 35 never-smokers. We observed significantly greater main (p = .01), right (p = .001) and total (p = .003) pulmonary artery volumes in ex-smokers with airflow obstruction as compared to ex-smokers without airflow obstruction. There were also significantly greater pulmonary artery volumes in both ex-smoker subgroups, compared to the never-smoker subgroup (p = .008). For all participants, there were significant correlations for pulmonary artery volumes with the ratio of the forced expiratory volume in 1 s to forced vital capacity (FEV1/FVC), the diffusing capacity of the lung for carbon monoxide (DLCO%pred), airway count, MRI ventilation defect percent and MRI apparent diffusion coefficients. In ex-smokers, ventilation defect percent was significantly correlated with right (r = 0.27, p = .02) and total (r = 0.25, p = .03) pulmonary artery volumes. Multivariate zero-inflated Poisson regression analysis showed that FEV1%pred (p = .004), DLCO%pred (p = .03), the six minute walk distance (p = .04) and total pulmonary artery volume (p = .03) were significant predictors of acute exacerbations of COPD, while the number of previous exacerbations was not. In conclusion, pulmonary artery enlargement measured using thoracic CT was observed even in ex-smokers without airflow obstruction and was predictive of COPD exacerbations in ex-smokers with airflow obstruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 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".