Low‐Dose Trazodone, Benzodiazepines, and Fall‐Related Injuries in Nursing Homes: A Matched‐Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether risk of fall-related injuries differs between nursing home (NH) residents newly dispensed low-dose trazodone and those newly dispensed benzodiazepines. DESIGN: Retrospective, matched cohort study in linked, population-based administrative data. Matching was based on propensity score ( ± 0.2 standard deviations of the score as a caliper), age ( ± 1 year), sex, frailty status, and history of dementia. The derived propensity score included demographic characteristics, clinical comorbidities, cognitive and functional status, and risk factors for falls. SETTING: All NHs in Ontario, Canada. PARTICIPANTS: Propensity score-matched pairs of residents aged 66 and older who received a full clinical assessment between April 1, 2010, and March 31, 2015 (N=7,791). MEASUREMENTS: Hospitalization (emergency department visit or acute care admission) for a fall-related injury within 90 days of exposure. Subdistribution hazard functions accounted for competing risk of death. Sensitivity analyses were used to examine falls resulting in hip or wrist fracture only, as well as different lengths of follow-up at 30, 60, and 180 days. RESULTS: Cumulative incidence of a fall-related injury in the 90 days after index was 5.7% for low-dose trazodone users and 6.0% for benzodiazepine users (between-group change=-0.29, 95% confidence interval (CI)=-1.02-0.44]; hazard ratio=0.94, 95% CI=0.83-1.08). Findings were consistent across sensitivity analyses. CONCLUSION: New use of low-dose trazodone was no safer with respect to a risk of a fall-related injury than new use of benzodiazepines. Additional studies to compare the effectiveness and risks of low-dose trazodone with those of a variety of psychotropic drug therapies are required in light of increasing trends in the use of trazodone in NHs.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".