Acute psychosis and the risk of motor vehicle crash
Bibliographic record
Abstract
ABSTRACT Importance Limited empirical evidence guides fitness-to-drive decision-making following an episode of acute psychosis. Objective To evaluate the association between acute psychosis and subsequent crash risk. Design Retrospective observational analyses using 20 years of population-based administrative health and driving data. We first assessed the association between psychosis and collisions using a case-crossover design, which controls for relatively fixed individual characteristics like personality, driving experience and routine driving habits. Next, we conducted a responsibility analysis which accounts for the changes in road exposure (miles of driving per month) that might occur after recent hospitalization. Setting British Columbia, Canada. Participants Drivers with a police-attended motor vehicle crash, 2000-2016. Exposure A hospital stay for acute psychosis ending in the 6-week interval prior to crash. Main Outcomes and Measures The case-crossover analysis examined crash involvement as a driver. The responsibility analysis examined driver responsibility for contributing to their crash. We used logistic regression with adjustment for potential confounders to evaluate associations between outcomes and recent acute psychosis. Results Among 9842 crashes in the case-crossover analysis, a hospital stay for acute psychosis ended in 199 pre-crash intervals and in 147 control intervals, suggesting acute psychosis was temporally associated with subsequent crash (2.0% vs 1.5% of intervals; adjusted odds ratio (aOR), 1.32; 95%CI, 1.05-1.66; p=0.02). Among 819,348 drivers with a police-attended crash and determinate crash responsibility, 178 of 235 drivers with a recent hospitalization for acute psychosis and 440,543 of 819,113 drivers without recent psychosis were deemed responsible for their crash (75.7% vs 53.8%; aOR, 2.38; 95%CI, 1.75-3.24; p<0.001). Conclusions The 6-week interval following a hospitalization for acute psychosis is associated with increased odds of crash and increased likelihood of a driver being deemed responsible for contributing to their crash. More stringent temporary driving restrictions after an episode of acute psychosis might reduce crash risk. KEY POINTS Questions Does a recent episode of acute psychosis increase a driver’s likelihood of being involved in a motor vehicle crash? Findings Using population-based administrative health and driving data, investigators found that the odds of crash were higher in the first 6 weeks after a hospital stay for acute psychosis than during control periods. A responsibility analysis accounting for changes in road exposure found drivers were also more likely to be deemed responsible for contributing to their crash during this period. Meaning More stringent driving restrictions in the first 6 weeks after an episode of acute psychosis might reduce crash risk.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".