The Potential Paradoxical Neurocognitive Effects of Cannabis Use in Patients with Psychotic Disorders: A Critical Meta-Review of Meta-Analytical Evidence
Bibliographic record
Abstract
Background: The association between cannabis and cognitive functions has been thoroughly studied in psychotic disorders, but conflictual/paradoxical results have emerged. This critical meta-review examined the magnitude of effects of cannabis on neurocognitive functions in patients with psychotic disorders provided by meta-analyses and evaluated the quality of evidence. Methods: A systematic search of meta-analyses was performed in PubMed, PsycINFO, Web of Science, and Google Scholar. Results: The search retrieved six meta-analyses. Quality of evidence varied from very low to moderate quality. No neurocognitive difference was observed between cannabis users and nonusers in first episode of psychosis samples. Limited evidence showed no significant difference in language, psychomotor functioning, and verbal/visual learning/memory, apart from improvements in verbal and visual memory (recognition). Findings showed better neurocognitive performances in cannabis-using patients for planning/reasoning and working memory. There were tendencies toward significance for processing speed and attention. Most effect sizes showed small to moderate degrees of outperformances in cannabis users. Individuals with lifetime use appeared to show better neurocognitive functions. Conclusion: Evidence indicated nonuniform effects of cannabis use across cognitive domains, with some areas suggesting better cognitive performances in cannabis users, entitled the paradoxical effect of the dually diagnosed. Clinical significance of these findings should be interpreted cautiously.
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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".