Lung and Heart Diseases Are Better Predicted by Pack-Years than by Smoking Status or Duration of Smoking Cessation in HIV Patients
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to assess the relationship of pack-years smoking and time since smoking cessation with risk of lung and heart disease. METHODS: We investigated the history of lung and heart disease in 903 HIV-infected patients who had undergone thoracic computed tomography (CT) imaging stratified by smoking history. Multimorbidity lung and heart disease (MLHD) was defined as the presence of ≥ 2 clinical or subclinical lung abnormalities and at least one heart abnormality. RESULTS: Among 903 patients, 23.7% had never smoked, 28.7% were former smokers and 47.6% were current smokers. Spirometry indicated chronic obstructive pulmonary disease in 11.4% of patients and MLHD was present in 53.6%. Age, male sex, greater pack-years smoking history and smoking cessation less than 5 years earlier vs. more than 10 years earlier (OR 2.59, 95% CI 1.27-5.29, p = 0.009) were independently associated with CT detected subclinical lung and heart disease. Pack-years smoking history was more strongly associated with MLHD than smoking status (p<0.001). CONCLUSIONS: MLHD is common even among HIV-infected patients who never smoked and pack- years smoking history is a stronger predictor than current smoking status of MLHD. A detailed pack-years smoking history should be routinely obtained and smoking cessation strategies implemented.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".