Risk of injurious road traffic crash after prescription of antidepressants
Bibliographic record
Abstract
Background Antidepressants are commonly used worldwide. Experimental studies have suggested that antidepressants may impair driving abilities. Aims/Objectives/Purpose The study aims to estimate the risk of road traffic crash associated with prescription of antidepressants. Methods Data from three French national databases were extracted and matched: the national health care insurance database, police reports, and the national police database of injurious crashes. A case-control analysis comparing 34 896 responsible versus 37 789 non-responsible drivers was conducted. Case-crossover analysis was performed to investigate the acute effect of medicine exposure. Results/Outcomes 72 685 drivers identified by their national healthcare number, involved in an injurious crash in France over the July 2005 to May 2008 period, were included. 2936 drivers (4.0%) were exposed to at least one antidepressant on the day of the crash. The results showed a significant association between the risk of being responsible for a crash and prescription of antidepressants (OR=1.34 (1.22 to 1.47)). The case-crossover analysis showed no association with treatment prescription but the risk of road traffic crash increased after an initiation of an antidepressant treatment (OR=1.49 (1.24 to 1.79)) and after a change in antidepressant treatment (OR=1.32 (1.09 to 1.60)). Significance/Contribution to the Field Patients and prescribers should be warned about the risk of crash during periods of antidepressant medication and particularly high vulnerability periods such as those when a treatment is initiated or modified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".