Mortality Associated with Benzodiazepines and Benzodiazepine-Related Drugs among Community-Dwelling Older People in Finland: A Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between the use of benzodiazepines (BDZs) and BDZ-related drugs and mortality among community-dwelling people aged 65 years and older in Finland. METHOD: This was a population-based retrospective cohort study. Records of all reimbursed drugs purchased by all 2224 residents of Leppävirta, Finland, aged 65 years and older in 2000 were extracted from the Finnish National Prescription Register. Diagnostic data were extracted from the Special Reimbursement Register. All-cause mortality was assessed after 9 years using national registers. Cox proportional hazards models were used to compute unadjusted and adjusted hazard ratios (HRs) and 95% confidence intervals for mortality among prevalent users of BDZs and BDZ-related drugs in 2000 (n = 325), compared with nonusers of BDZs and BDZ-related drugs between 2000 and 2008 (n = 1520). RESULTS: BDZs and BDZ-related drugs were used by 325 out of the 2224 residents (14.6%) in 2000. The 9-year mortality was 50.2% among BDZ and BDZ-related drug users in 2000 and 36.3% among BDZ and BDZ-related drug nonusers between 2000 and 2008 (HR 1.53; 95% CI 1.28 to 1.82). After adjusting for baseline age, sex, antipsychotic drug use, and diagnostic confounders, the HR was 1.01 (95% CI 0.84 to 1.21). CONCLUSIONS: Use of BDZs and BDZ-related drugs was associated with an increased mortality hazard in unadjusted analyses. However, after adjusting for age, sex, antipsychotic drug use, and diagnostic confounders, the use of BDZs and BDZ-related drugs was not associated with excess mortality.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".