Effects of Inhaled Tobacco and Cannabis Co-Use on Respiratory Health and Tobacco Cessation: An Official American Thoracic Society Research Statement
Bibliographic record
Abstract
Abstract Background Tobacco and cannabis are among the most widely used substances globally, and rates of co-use are on the rise. Understanding the impact of inhaled tobacco-cannabis co-use on health outcomes and tobacco cessation is critical for guiding patients and clinicians. Objectives To summarize the existing evidence, identify knowledge gaps, and prioritize research questions related to effects of inhaled tobacco-cannabis co-use on tobacco cessation and lung health. Methods A multidisciplinary committee was convened to review the evidence, identify knowledge gaps, and develop research questions in four priority research areas: 1) common data elements and terminology, 2) patterns and prevalence of co-use, 3) impact of co-use on tobacco cessation, and 4) effects of co-use on lung health. A modified Delphi process was conducted in three rounds to reach consensus on prioritizing research questions. Results The evidence reviewed by the expert panel in four priority research areas yielded the following gaps in the literature with high priority to address with future research: 1) lack of consensus on terminology and recommended co-use data elements, 2) limited research on co-use and tobacco-related disparities, 3) insufficient evidence on how cannabis use affects tobacco cessation, and 4) alarming yet inconsistent findings on the effects of co-use on lung health. Conclusions This statement outlines and guides a research agenda on the effects of inhaled tobacco-cannabis co-use on tobacco cessation and lung health. Consensus-driven recommendations include adopting harmonized terms and minimum data elements, studying the prevalence of co-use among populations experiencing tobacco-related disparities, evaluating the impact of co-use on tobacco cessation pharmacotherapies, and assessing the effects of co-use on the development and progression of lung diseases.
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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 | 0.172 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".