The effects of marijuana smoking on lung function in older people
Bibliographic record
Abstract
Background Previous studies have associated marijuana exposure with increased respiratory symptoms and chronic bronchitis among long-term cannabis smokers. The long-term effects of smoked marijuana on lung function remain unclear. Methods We determined the association of marijuana smoking with the risk of spirometrically defined chronic obstructive pulmonary disease (COPD) (post-bronchodilator forced expiratory volume in 1 s (FEV1)/forced vital capacity ratio <0.7) in 5291 population-based individuals and the rate of decline in FEV1in a subset of 1285 males and females, aged ≥40 years, who self-reported use (or non-use) of marijuana and tobacco cigarettes and performed spirometry before and after inhaled bronchodilator on multiple occasions. Analysis for the decline in FEV1was performed using random mixed effects regression models adjusted for age, sex and body mass index. Heavy tobacco smoking and marijunana smoking was defined as >20 pack-years and >20 joint-years, respectively. Results ∼20% of participants had been or were current marijuana smokers with most having smoked tobacco cigarettes in addition (83%). Among heavy marijuana users, the risk of COPD was significantly increased (adjusted OR 2.45, 95% CI 1.55–3.88). Compared to never-smokers of marijuana and tobacco, heavy marijuana smokers and heavy tobacco smokers experienced a faster decline in FEV1by 29.5 mL·year−1(p=0.0007) and 21.1 mL·year−1(p<0.0001), respectively. Those who smoked both substances experienced a decline of 32.31 mL·year−1(p<0.0001). Interpretation Heavy marijuana smoking increases the risk of COPD and accelerates FEV1decline in concomitant tobacco smokers beyond that observed with tobacco alone.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".