A systematic review of longitudinal studies investigating the impact of cannabis use in patients with psychotic disorders
Bibliographic record
Abstract
Introduction: Research has established a link between cannabis use and adverse psychotic outcomes in psychosis patients. However, we have yet to determine if this relationship is maintained when controlling for important confounding variables. The following systematic review aims to investigate if the association between cannabis use and psychotic outcomes is preserved when accounting for important confounders, and if discontinued use mitigates any potential negative impacts.Areas covered: The authors conducted an exhaustive search of the MEDLINE database and Google Scholar to identify articles pertaining to the systematic review. Thirty-three articles were retained for meeting the eligibility criteria.Expert opinion: The evidence confirms an overarching pattern of negative psychotic outcomes of cannabis intake in psychosis populations, even when accounting for crucial confounders. Psychosis patients should be informed with evidence-based health information regarding the effects of cannabis use. Clinicians should systematically evaluate cannabis intake patterns in psychosis patients and offer intervention services geared toward reducing problematic consumption. Researchers should record confounding factors in a more systematic manner in future longitudinal investigations while paying careful attention to the potency and dose–response effects of the ingested cannabis. Deciders will need to investigate the impact of cannabis regulations on psychosis populations.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".