Assessing the cumulative effects of exposure to selected benzodiazepines on the risk of fall-related injuries in the elderly
Bibliographic record
Abstract
BACKGROUND: The use of benzodiazepines is associated with increased risk of fall-related injuries in the elderly. However, it is unclear if the risks vary across the products and how they depend on the pattern of use and dosage. Specifically, the possibility of cumulative effects of past benzodiazepine use has not been thoroughly investigated. METHODS: We used the administrative database for a cohort of 23,765 new users of benzodiazepines, aged 65 years and older, in Quebec, Canada, between 1990 and 1994. The associations between the use of seven benzodiazepines and the risk of fall-related injuries were assessed using several statistical models, including a novel weighted cumulative exposure model. That model assigns to each dose taken in the past a weight that represents the importance of that dose in explaining the current risk of fall. RESULTS: For flurazepam, the best-fitting model indicated a cumulative effect of doses taken in the last two weeks. Uninterrupted use of flurazepam in the past months was associated with a highly significant increase in the risk of fall-related injuries (HR = 2.83, 95% CI: 1.45-4.34). The cumulative effect of a 30-day exposure to alprazolam was 1.27 (1.13-1.42). For temazepam, the results suggested a potential withdrawal effect. CONCLUSIONS: Mechanisms affecting the risk of falls differ across benzodiazepines, and may include cumulative effects of use in the previous few weeks. Thus, benzodiazepine-specific analyses that account for individual patterns of use should be preferred over simpler analyses that group different benzodiazepines together and limit exposure to current use or current dose.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".