892 – The Association Between Cannabis Use And Depression: a Systematic Review And Meta-analysis Of Longitudinal Studies
Bibliographic record
Abstract
Background Longitudinal studies reporting the association between cannabis use and developing depression provide mixed results. The objective of this study was to establish the extent to which different patterns of use of cannabis are associated with the development of depression using meta-analysis of longitudinal studies. Methods Peer-reviewed publications that compared the risk of development of depression in cannabis users and non-userst were located using searches of EMBASE, MEDLINE, PsychINFO and ISI Web of Science. Data on measures of cannabis use, measures of depression and control variables were extracted. Odds ratios were extracted by age and length of follow-up. Results After screening 3,905 articles, 55 articles were selected for full-text review, of which 12 were included in the quantitative analysis. The odds for cannabis users developing depression compared to controls was 1.26 (95%CI=1.10-1.44). The odds for heavy cannabis users developing depression was 1.72 (95%CI=1.27-2.34), compared to non-users or light users. Meta-regression revealed no significant differences in effect based on age of subjects or length of follow-up in the individual studies. There was large heterogeneity in the number and type of control variables in the different studies. Conclusions Cannabis use, and particularly heavy cannabis use, may be associated with an increased risk for developing depressive disorders. Despite limitations due to heterogeneity in control variables, this study represents the current state of knowledge on this association. In order to establish a more precise dose-response relationship between cannabis use and the risk of developing depression, future longitudinal exploration should take into account cumulative exposure to cannabis.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.029 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".