A review of co‐morbid tobacco and cannabis use disorders: Possible mechanisms to explain high rates of co‐use
Bibliographic record
Abstract
BACKGROUND: Tobacco and cannabis are among the most commonly used psychoactive substances worldwide, and are often used in combination. Evidence suggests that tobacco use contributes to an increased likelihood of becoming cannabis dependent and similarly cannabis use promotes transition to more intensive tobacco use. Further, tobacco use threatens cannabis cessation attempts leading to increased and accelerated relapse rates among cigarette smokers. Given that treatment outcomes are far from satisfactory among individuals engaged in both tobacco and cannabis use highlights the need for further exploration of this highly prevalent co-morbidity. OBJECTIVE: Therefore, this review will elucidate putative neurobiological mechanisms responsible for facilitating the link between co-morbid tobacco and cannabis use. METHOD: We performed an extensive literature search identifying published studies that examined co-morbid tobacco and cannabis use. RESULTS: Evidence of both synergistic and compensatory effects of co-morbid tobacco and cannabis use have been identified. Following, co-morbid use of these substances will be discussed within the context of two popular theories of addiction: the addiction vulnerability hypothesis and the gateway hypothesis. Lastly, common route of administration is proposed as a facilitator for co-morbid use. CONCLUSIONS & SCIENTIFIC SIGNIFICANCE: While, only a paucity of treatment studies addressing co-morbid tobacco and cannabis use have been conducted, emerging evidence suggests that simultaneously quitting both tobacco and cannabis may yield benefits at both the psychological and neurobiological level. More research is needed to confirm this intervention strategy and future studies should consider employing prospective systematic designs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".