Comparative safety of chronic versus intermittent benzodiazepine prescribing in older adults: A population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Benzodiazepine treatment recommendations for older adults differ markedly between guidelines, especially their advice on the acceptability of long-term use. AIMS: Using population-based data we compared risks associated with chronic versus intermittent benzodiazepine usage in older adults. The primary outcome was falls resulting in hospital/emergency department visits. METHODS: We undertook a retrospective population-based cohort study using linked healthcare databases in adults aged ⩾ 66 years in Ontario, Canada, with a first prescription for benzodiazepines. Chronic and intermittent benzodiazepine users, based on the 180 days from index prescription, were matched (1:2 ratio) by sex, age and propensity score, then followed for up to 360 days. Hazard ratios (HRs) for outcomes were calculated from Cox regression models. RESULTS: < 0.0001). There were significant excess risks in chronic users for most secondary outcomes: hip fractures, hospitalizations/emergency department visits, long-term care admission and death, but not wrist fractures. Adjustment for benzodiazepine dosage had minimal impact on HRs. CONCLUSION: Our study demonstrates evidence of significant excess risks associated with chronic benzodiazepine use compared to intermittent use. The excess risks may inform decision-making by older adults and clinicians about whether short- or long-term benzodiazepine use is a reasonable option for symptom management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".