Cannabis and nicotine/tobacco co-use and its association with cognitive and neural outcomes: A systematic review
Bibliographic record
Abstract
BACKGROUND: Cannabis and nicotine/tobacco products (NTP) are among the most widely used substances worldwide and are commonly used together. The rising rates of co-use have resulted in a growing number of studies examining the association between cannabis and NTP co-use and cognitive and brain outcomes. Therefore, we synthesized findings from studies investigating the associations between co-use and cognition, brain structure, brain function, and molecular outcomes. METHODS: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched four databases for human and animal studies examining the relationship between co-use and cognition, brain structure, brain function, and molecular outcomes. RESULTS: Thirty-nine studies were reviewed. Patterns emerged across cognitive and functional studies, such that people who co-use showed similar working memory performance, resting-state connectivity, and task-based activation compared to people without substance use. Structural and molecular findings were heterogeneous and inconsistent. Animal research was sparse, with no studies examining brain structure or function. While molecular outcomes were mixed and often contradicted human findings, results on memory were more consistent and aligned with human data. CONCLUSIONS: Although findings were heterogenous, converging evidence suggests that cannabis and NTP use may offset each other in cognitive and brain functional outcomes, mitigating impairments linked to single-substance use. These results offer novel insight into the cognitive and neurobiological factors that may underlie co-use and may help inform treatment. Rather than extrapolating cannabis and NTP co-use treatment from single-substance treatment approaches, interventions targeting the unique mechanisms of co-use may prove most effective.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".