Cannabis consumption and motor vehicle collision: A systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: Increasing legalization of recreational cannabis and availability of cannabinoid products has resulted in expanded use, which is associated with adverse effects including concerns over increased risk of motor vehicle collision (MVC). We aimed to explore the association between cannabis consumption and MVC. METHODS: We searched MEDLINE, EMBASE, CINAHL, Cochrane library, SCOPUS, PsycInfo, Web of Science, TRID from inception to November 2024. We included studies assessing the association between cannabis consumption on MVC fatalities, any injuries, and culpability/unsafe driving actions. Pairs of reviewers independently screened search results, extracted data, and assessed risk of bias. We used a DerSimonian and Laird random-effects model for all meta-analyses and the GRADE approach to assess the certainty of evidence. RESULTS: We included 31 studies with 328,388 individuals. Low certainty evidence suggests that cannabis consumption may be associated with an increased risk of MVC fatality (8 studies, OR 1.55, 95% CI: 1.20 to 1.98) with an absolute risk increase (ARI) of 14 more deaths per 100,000 MVC's. Low certainty evidence from 9 case-control studies suggests cannabis consumption may be associated with an increased risk of injury due to MVC (OR 2.00, [95% CI: 1.31-3.07]; absolute risk increase of 6.8%). We are uncertain about the association of cannabis consumption with MVC culpability/unsafe driving action as the evidence was only very low certainty. CONCLUSIONS: Low certainty evidence suggests that cannabis consumption may increase risk of MVC fatality and risk of injury from MVC. The association between cannabis use and risk of unsafe driving is uncertain. PROTOCOL REGISTRATION: CRD42022357478.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.009 | 0.011 |
| 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) | 0.004 | 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".