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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".