Prescription of antidepressants and the risk of road traffic crash in the elderly: a case–crossover study
Bibliographic record
Abstract
AIM: To investigate the impact of antidepressants on the risk of road traffic crash in the elderly. METHODS: Reports from the Universal Quebec Automobile Insurance Agency database were matched with data on antidepressant prescription from the Quebec Health Insurance Agency. The case-crossover analysis consisted in comparing exposure during a period immediately before the crash with exposure during earlier periods, for the same subject. RESULTS: One hundred and nine thousand four hundred and six drivers between 66 and 84 years of age involved in a traffic crash between 1988 and 2000 were included. Two thousand nine hundred and nineteen (2.7%) were exposed to an antidepressant on the day of the crash. Case-crossover analysis found an increased risk of crash in drivers with a prescription of antidepressants before their crash when compared with a prescription of antidepressants 4 to 8 months before the crash (OR = 1.19, 95% CI 1.08, 1.30 to 1.42. 95% CI 1.30, 1.55). With the most recent control periods, results were not significant. CONCLUSION: A patient's mental state is probably more similar between two periods that are close to each other than up to 8 months before. Consequently, the risk of crash is likely to be linked to symptoms of depression.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".