Antipsychotic treatment adherence and motor vehicle crash among drivers with schizophrenia: a case–crossover study
Bibliographic record
Abstract
BACKGROUND: Among individuals with schizophrenia, antipsychotic medications improve performance across several cognitive and functional domains. We sought to assess whether antipsychotic adherence reduces the risk of a motor vehicle crash. METHODS: We performed a case-crossover study using population-based administrative health and driving data from British Columbia, Canada. We included individuals with schizophrenia who were involved as a driver in a police-attended motor vehicle crash during a 15-year interval (2001-2016) and who filled prescriptions for antipsychotic medication as an outpatient in the 2 years before the crash. We measured adherence to antipsychotic treatment by using prescription fill data to calculate the medication possession ratio (MPR) in the 30 days before the crash (the pre-crash interval) and in a 30-day control interval ending 1 year before the crash. We used conditional logistic regression to evaluate the association between MPR and motor vehicle crash after adjusting for potential confounders. RESULTS: Among 1130 eligible motor vehicle crashes involving drivers with schizophrenia, the mean antipsychotic MPR was 0.69 in the pre-crash intervals and 0.76 in the control intervals. We found that perfect adherence to antipsychotic medication was associated with half the odds of a crash relative to complete nonadherence (adjusted odds ratio 0.50, 95% confidence interval 0.38-0.66). The findings were consistent among subgroups defined by sex, age, and history of alcohol or drug misuse. INTERPRETATION: Better adherence to antipsychotic medications was associated with lower crash risk. Physicians and fitness-to-drive policy-makers might consider antipsychotic treatment adherence as a condition for maintaining an active driver's licence among individuals with schizophrenia.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".