Health impacts of cannabis: focus on smoking vs. vaping effects on the respiratory and cardiovascular systems
Bibliographic record
Abstract
PURPOSE OF REVIEW: The legalization of recreational and medical cannabis has shifted consumption patterns, with vaping emerging as a common alternative to smoking. This review summarizes recent evidence comparing the respiratory and cardiovascular health effects of these two modes of administration. RECENT FINDINGS: Vaping reduces exposure to combustion-related toxicants and allows modulation of Δ 9 -tetrahydrocannabinol (THC) bioavailability compared to smoking cannabis. However, both modes of consumption produce comparable physiological effects, including acute increases in heart rate and blood pressure, and altered immune responses in lung. There is clear association between THC vaping and e-cigarette or vaping associated lung injury. Vaping is also linked to increased risk of respiratory symptoms. Evidence regarding the long-term risks of vaping cannabis such as chronic obstructive pulmonary disease, lung cancer, myocardial infarction and stroke remains limited and inconclusive. SUMMARY: While vaping cannabis may mitigate some combustion-related harms compared to smoking, it introduces distinct respiratory and cardiovascular concerns. Evidence on long-term comparative safety remains limited, highlighting the need for longitudinal and experimental studies examining health outcomes across different modes of consumption, devices, temperatures and formulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".