Cigarette Smoking Is Associated with Subclinical Parenchymal Lung Disease
Bibliographic record
Abstract
RATIONALE: Cigarette smoking is a risk factor for diffuse parenchymal lung disease. Risk factors for subclinical parenchymal lung disease have not been described. OBJECTIVES: To determine if cigarette smoking is associated with subclinical parenchymal lung disease, as measured by spirometric restriction and regions of high attenuation on computed tomography (CT) imaging. METHODS: We examined 2,563 adults without airflow obstruction or clinical cardiovascular disease in the Multi-Ethnic Study of Atherosclerosis, a population-based cohort sampled from six communities in the United States. Cumulative and current cigarette smoking were assessed by pack-years and urine cotinine, respectively. Spirometric restriction was defined as a forced vital capacity less than the lower limit of normal. High attenuation areas on the lung fields of cardiac CT scans were defined as regions having an attenuation between -600 and -250 Hounsfield units, reflecting ground-glass and reticular abnormalities. Generalized additive models were used to adjust for age, gender, race/ethnicity, smoking status, anthropometrics, center, and CT scan parameters. MEASUREMENTS AND MAIN RESULTS: The prevalence of spirometric restriction was 10.0% (95% confidence interval [CI], 8.9-11.2%) and increased relatively by 8% (95% CI, 3-12%) for each 10 cigarette pack-years in multivariate analysis. The median volume of high attenuation areas was 119 cm(3) (interquartile range, 100-143 cm(3)). The volume of high attenuation areas increased by 1.6 cm(3) (95% CI, 0.9-2.4 cm(3)) for each 10 cigarette pack-years in multivariate analysis. CONCLUSIONS: Smoking may cause subclinical parenchymal lung disease detectable by spirometry and CT imaging, even among a generally healthy cohort.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".