Differential Effects of Cannabis and Tobacco on Lung Function in Mid–Adult Life
Bibliographic record
Abstract
Abstract Rationale Evidence suggests that the effects of smoking cannabis on lung function are different from tobacco. However, long-term follow-up data are scarce and mostly based on young adults. Objectives To assess the effects of cannabis and tobacco on lung function in mid–adult life. Methods Cannabis and tobacco use were reported at ages 18, 21, 26, 32, 38, and 45 years in a population-based cohort study of 1,037 participants. Spirometry, plethysmography, and carbon monoxide transfer factor were measured at age 45. Associations between lung function and cannabis use were adjusted for tobacco use. Measurements and Main Results Data were available from 881 (88%) of 997 surviving participants. Cumulative cannabis use was associated with lower FEV1/FVC ratios, owing to a tendency toward higher FVCs. Cannabis use was also associated with higher TLC, FRC, residual volume, and Va along with lower midexpiratory flows, airway conductance, and transfer factor. Quitting regular cannabis use between assessments was not associated with changes in spirometry. Conclusions Cannabis use is associated with higher lung volumes, suggesting hyperinflation. There is evidence of increased large-airway resistance and lower midexpiratory airflow, but impairment of FEV1/FVC ratio is because of higher FVC. This pattern of effects is different to those of tobacco. We provide the first evidence that lifetime cannabis use may be associated with impairment of gas transfer.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".