To What Extent Does Adding Tobacco to Cannabis Expose Young Users to Nicotine?
Bibliographic record
Abstract
INTRODUCTION: To determine if mulling, the process of adding tobacco to cannabis for its consumption, exposes young cannabis users to significant levels of nicotine. METHODS: This observational study performed in 2009-2010 among Swiss youths aged 16-25 years involved the completion of a self-administrated questionnaire and the collection of a urine sample on the same day. Measures of urinary cotinine were blindly performed using liquid chromatography coupled-mass spectrometry. A total of 197 eligible participants were divided in 3 groups based on their consumption profile in the past 5 days: 70 abstainers (ABS) not having used cigarettes or cannabis, 57 cannabis users adding tobacco to the cannabis they smoke (MUL) but not having smoked cigarettes, and 70 cigarette smokers (CIG) not having smoked cannabis. RESULTS: Exposure to nicotine was at its lowest among ABS with a mean (SE) cotinine level of 3.2 (1.4) ng/ml compared, respectively, with 214.6 (43.8) and 397.9 (57.4) for MUL and CIG (p < .001). While consumption profile appeared as the only significant factor of influence when examining nicotine exposure from the ABS and MUL participants on multivariate analysis, it did not result in substantial differences among MUL and CIG groups. CONCLUSIONS: Urinary cotinine levels found among MUL are high enough to indicate a significant exposure to nicotine originating from the mulling process. In line with our results, health professionals should pay attention to mulling as it is likely to influence cannabis and cigarette use as well as the efficacy of cessation interventions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".