The association between legalization of cannabis use and traffic deaths in Uruguay
Bibliographic record
Abstract
BACKGROUND AND AIMS: While cannabis use has been found to impair motor vehicle driving, the association between cannabis legalization and motor vehicle fatalities is unclear. In Uruguay in December 2013, cannabis for recreational purposes was legalized. This study assessed the association between implementation of this law and changes in traffic fatality rates. DESIGN: Interrupted time-series analysis of traffic fatality rates of light motor vehicle drivers and motorcyclists in urban and rural settings. Changes are reported as step and trend effects against modeled trends in the absence of legalization. SETTING: Uruguay, Montevideo and four rural provinces (Colonia, Florida, Río Negro and San José) from 1 January 2012 to 31 December 2017. Cases and measurement Weekly traffic fatalities of light motor vehicle drivers and motorcyclists per type of vehicle. Data were gathered from the National Road Safety Agency of Uruguay and the Ministry of Transport and Public Works, respectively. RESULTS: Cannabis legalization was associated with a 52.4% immediate increase [95% confidence interval (CI) = 11.6, 93.3, P = 0.012] in the light motor vehicle driver's fatality rate. However, no significant change in the motorcyclists' fatality rate was observed. In Montevideo the legislation was associated with an absolute increase in its light motor vehicle driver's fatality rate by 0.06 (95% CI = 0.01, 0.11, P = 0.025), but no significant associations were observed in rural settings. CONCLUSIONS: In Uruguay, the 2013 legislation legalizing recreational cannabis consumption may have been associated with an increase in fatal motor vehicle crashes, particularly in light motor-vehicle drivers and urban settings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".