A review of co‐morbid tobacco and cannabis use disorders: Possible mechanisms to explain high rates of co‐use
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it