Benzodiazepine utilization patterns in Alzheimer's disease patients
Bibliographic record
Abstract
BACKGROUND: Benzodiazepines (BZD) are commonly prescribed in the elderly. Persons with dementia may be at a greater risk of adverse reactions of BZD such as cognitive impairment. OBJECTIVE: To assess the prevalence of BZD use in Alzheimer's disease patients and to examine patient and drug-characteristics associated with this use. DESIGN: Cross-sectional study. PARTICIPANTS: Five thousand community-dwelling and institutionalized patients initiating a treatment with tacrine for a mild to moderate Alzheimer's disease and included in the tacrine-study (Paco cohort). MEASUREMENTS: Patient characteristics and BZD use recorded at the inclusion. MAIN OUTCOME: Use of BZD during the 3 months prior to inclusion. RESULTS: The 3-month prevalence of ever use of BZD was 20%. After controlling for age and gender, there was a non-significant inverse association between BZD use and a score of Mini-Mental Status Evaluation (MMSE) below 24 (OR: 0.88, 95% CI: 0.71-1.09), and significant inverse association with an increased number of chronic conditions (OR: 0.73, 95% CI: 0.58-0.91). Higher use of BZD was associated with higher level of overall drug consumption (OR: 2.3, 95% CI: 1.97-2.80). CONCLUSION: Alzheimer's disease patients are frequently prescribed BZD. A low score of MMSE (< 24) is associated with a decreased use of BZD. These results suggest important differences in BZD use patterns among persons with Alzheimer's disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".