Cannabis vaporisation: Understanding products, devices and risks
Bibliographic record
Abstract
ISSUES: Vaporisation is a common method of cannabis administration. Inconsistent terminology and jargon regarding vaporisation has led to confusion. The increasing public interest and access to cannabis, combined with possible safety concerns associated with certain cannabis vaping products, warrants improved consumer and public and health care professional knowledge. APPROACH: To improve this knowledge, we conducted a review of the common terminology, regulatory status, products and device types related to cannabis vaporisation. KEY FINDINGS: Cannabis vaporisation devices can be separated into nine types. While vaporisation reduces respiratory risks associated with cannabis combustion, not all vaping products and device types carry the same level of safety. Metered dose inhalers and dried product vaporisers present the lowest safety risk due to a lower risk of toxin exposure and the use of lower tetrahydrocannabinol potency products. IMPLICATIONS: As both vaping and cannabis use increase in popularity, focusing on accurate health education will help facilitate health promotion to encourage lower risk use. The current lack of understanding on risk differences between types of cannabis vaporisation is a missed opportunity for harm reduction. Increased opportunities for public health and health care professional education on different cannabis vaporisation devices and associated risks are warranted. Improvements to health warning labelling may also be beneficial. CONCLUSION: Not all cannabis vaporisation devices and products carry the same level of risk. A better understanding of risk differentiation is needed among consumers and health professionals. Continued research, policy development and health education can lead to safer cannabis vaporisation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".