The effects of cannabis use disorder on cognitive functions: A meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Impairments in cognition are frequently associated with acute cannabis consumption; on the other hand, controversies persist regarding the residual cognitive impairments of cannabis, with some estimates highlighting significant or mild cognitive impairment. One of the main limitations of the available research syntheses is that little attention has been paid to individuals with cannabis use disorder. Thus, our main objectives are to determine the amplitude of the cognitive deficits associated with cannabis use disorder, and to identify the cognitive domains the most and least impaired. METHODS: Studies with a patient group with a cannabis use disorder diagnosis and data from at least one validated neurocognitive test were selected. After manual extraction, data were pooled in a multivariate meta-analysis and effect size estimates were calculated for 13 cognitive domains. Meta-regression analyses on potential moderators were performed. FINDINGS: There were small-to-moderate impairments in 10 out of the 13 cognitive domains. Deficits in verbal learning/memory, speed of processing and working memory were more prominent (d = 0.4/0.5) whereas verbal fluency and attention were the least affected. No association was observed between the potential moderators and global cognition. CONCLUSION: This meta-analysis shows that cannabis use disorder is associated with moderate deficits in verbal learning/memory, speed of processing and working memory. Despite the limitation of the studies in the field, our results should serve as a reminder that the residual cognitive impairments associated with cannabis should not be under-estimated prematurely.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.035 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".