Concurrent Use of Benzodiazepines and Antidepressants and the Risk of Motor Vehicle Accident in Older Drivers: A Nested Case–Control Study
Bibliographic record
Abstract
INTRODUCTION: Aging of the population results in an increase in senior drivers. Elderly are frequently treated with benzodiazepines and antidepressants. The objective of this study was to determine whether the concurrent use of benzodiazepines and antidepressants is associated with motor vehicle accidents (MVAs) in the elderly. METHODS: This was a nested case-control study within a cohort of drivers aged 67-84 years between 1990 and 2000, identified from the Société de l'Assurance Automobile du Québec and the Régie de l'Assurance Maladie du Québec databases. First cases of MVAs during follow-up were matched with up to ten controls from the cohort. Odds ratios (ORs) for the association between MVA and the use of benzodiazepines and antidepressants were estimated using conditional logistic regression. RESULTS: The cohort included 373,818 drivers, with 74,503 MVA cases matched with 744,663 controls. The risk of MVA was higher in current users of long-acting benzodiazepines [OR 1.23; 95% confidence interval (CI) 1.16-1.29] than in current users of short-acting benzodiazepines (OR 1.05; 95% CI 1.02-1.08). The risk of MVA was increased in current users of selective serotonin reuptake inhibitors (SSRIs; OR 1.13; 95% CI 1.04-1.22), while it was not in current users of tricyclic antidepressants (TCAs; OR 1.04; 95% CI 0.96-1.14). The highest ORs of MVA were observed in long-acting benzodiazepines users concurrently using SSRIs (OR 1.37; 95% CI 1.07-1.77, P value for interaction = 0.964) or TCAs (OR 1.54; 95% CI 1.21-1.95, P value for interaction = 0.077). CONCLUSION: Use of long-acting benzodiazepines is associated with an increased risk of MVA in the elderly, particularly in those concurrently using SSRIs or TCAs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".