Comparative Risk of Harm Associated with Zopiclone or Trazodone Use in Nursing Home Residents: a Retrospective Cohort Study in Alberta, Canada
Bibliographic record
Abstract
Background: There is growing evidence of harm associated with trazodone and nonbenzodiazepine sedative hypnotics (e.g., zopiclone); however, their comparative risk of harm is unknown. Methods: We conducted a retrospective cohort study with linked health administrative data, which enrolled older (≥66 years old) nursing home residents living in Alberta, Canada, between December 1, 2009, and December 31, 2018; the last follow-up date was June 30, 2019. We compared the rate of injurious falls and major osteoporotic fractures (primary outcome) and all-cause mortality (secondary outcome) within 180 days of first prescription of zopiclone or trazodone with cause-specific hazard models and inverse probability of treatment weights to control for confounding; primary analysis was intention-to-treat and secondary analysis was per-protocol (i.e., residents censored if dispensed the other exposure drug). Results: Our cohort included 1,403 residents newly dispensed trazodone and 1,599 residents newly dispensed zopiclone. At cohort entry, the mean resident age was 85.7 (standard deviation [SD] 7.4), 61.6% were female, and 81.2% had dementia. New zopiclone use was associated with similar rates of injurious falls and major osteoporotic fractures (intention-to-treat-weighted hazard ratio 1.15, 95% confidence interval [CI] 0.90-1.48; per-protocol-weighted hazard ratio 0.85, 95% CI 0.60-1.21) and all-cause mortality (intention-to-treat-weighted hazard ratio 0.96, 95% CI 0.79-1.16; per-protocol-weighted hazard ratio 0.90, 95% CI 0.66-1.23) compared to trazodone. Conclusions: Zopiclone was associated with a similar rate of injurious falls, major osteoporotic fractures, and all-cause mortality compared to trazodone-suggesting one medication should not be used in lieu of the other. Appropriate prescribing initiatives should also target zopiclone and trazodone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".