Prevalence and potential consequences of benzodiazepine use in senior citizens: results from the Canadian Study of Health and Aging.
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate changes in benzodiazepine use over time, and the association between benzodiazepine use and select outcomes. DESIGN: A five-year longitudinal cohort study in subjects 65 years of age and older. SETTING: Select urban communities and institutions across Canada with senior citizens. PARTICIPANTS: Subjects who were first seen in 1990 to 1991, recontacted in 1996, and agreed to undergo a second clinical examination. Mortality rates were based on the initial 2914 subjects enrolled. MEASUREMENTS: Number and type of medications used. Outcomes (mortality, incident institutionalization, change in cognition, depression, function, self-rated health) associated with benzodiazepine use. Logistic regression to predict outcomes and pattern of benzodiazepine use. RESULTS: Mean number of medications being taken by senior citizens increased to 5.8 from 3.9. The proportion of subjects using benzodiazepines at time 1 and time 2 was similar (26.4% versus 25.2%). Affect, self-rated health, cognition, function and incident institutionalization were significantly associated with benzodiazepine use. Subjects with a depressed mood were more likely to be prescribed a benzodiazepine (37%) than an antidepressant (26.9%). CONCLUSION: Benzodiazepines were associated with a number of adverse outcomes. The relative benefits and risks of benzodiazepine use in an older population should be re-examined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".