Benzodiazepines and elderly drivers: a comparison of pharmacoepidemiological study designs
Bibliographic record
Abstract
PURPOSE: Contradictory results were published from two studies in the late 1990s about the effects of long half-life benzodiazepine use on the risk of motor vehicle crashes (MVCs) in the elderly. The use of different study designs could explain the differences observed in these studies. METHODS: The results of an unmatched case-control study were compared to those of a case-crossover study using the same prescription claims database to determine whether the current use of benzodiazepines increased the risk of MVCs. RESULTS: There were 5579 cases and 12 911 controls identified between the years 1990 and 1993 in the province of Quebec, Canada. The case-control approach demonstrated an increased rate of injurious MVC associated with the current use of long-acting benzodiazepines [odds ratio (OR) 1.45; 95% confidence interval (CI): 1.12-1.88]. The case-crossover approach applied to all cases did not show any association [OR 0.99; 95%CI: 0.83-1.19]. However, among the cases restricted to subjects with four or less prescriptions filled in the previous year, corresponding more to transient exposures, the OR was elevated [OR 1.53; 95%CI: 1.08-2.16]. CONCLUSIONS: Differences in study design and analysis may explain some of the discrepancies in previous results. Both study designs provide evidence that long-acting benzodiazepines appear to be associated with an increased risk of MVC.
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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 | 0.052 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| 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".