Uncovering the source of new benzodiazepine prescriptions in community‐dwelling older adults’
Bibliographic record
Abstract
OBJECTIVE: Initiatives to reduce benzodiazepine use have been largely unsuccessful despite strong associations with adverse outcomes. Curtailing incident use of benzodizepines is an alternate strategy that has yet to be explored. This study aims to determine the source of incident benzodiazepine prescriptions by comparing the risk of receiving a new prescription upon hospital discharge versus after an ambulatory care clinic visit. METHODS: Data were derived from 1189 community-dwelling adults aged 65 years naive to benzodiazepine consumption, enrolled in the Étude sur la Santé des Ainés, a prospective 3-year cohort study conducted in Québec, Canada. Health survey questionnaires were linked with provincial administrative databases of prescription and health service claims. Analysis with multivariate Poisson regression models compared the risk of incident benzodiazepine use post-hospitalization versus after an ambulatory care visit. Models were adjusted for sex, age, antidepressant use, and concomitant drugs. Sub-analyses were conducted for chronic prescriptions. RESULTS: Incident benzodiazepine use was 11% over a 2-year period, with 18.3% of prescriptions leading to chronic use (> 90 days). Hospitalization conferred a 2.7-fold greater risk of incident use than an outpatient visit (OR 2.66, 95% CI 1.78-3.98) and a 4.7-fold (OR 4.74, 95% CI 1.63-13.78) increased risk of chronic use, after adjusting for potential confounders. Despite the increased risk, only 13% of new prescriptions originated post-hospital discharge, with the remainder prescribed during outpatient visits. CONCLUSION: Interventions are required to curb incident benzodiazepine prescriptions at their source both in hospitals and in ambulatory care settings.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".