Hypnotics and Triazolobenzodiazepines - Best Predictors of High-Dose Benzodiazepine Use: Results from the Luxembourg National Health Insurance Registry
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Benzodiazepines are not all the same concerning their risk of high-dose use. METHODS: We studied benzodiazepine use from the Luxembourg national records of all insured. We calculated the 12-year prevalence from 1995 to 2007. Benzodiazepine users were divided into 3 groups, short-term with no longer than 3-month intake, intermediate with multiple administration with at least a 1-year interruption, and continuous who never stopped. A high-dose user (HDU) was defined as a patient who received a higher dose than the yearly maximum usual therapeutic dose. RESULTS: An average of 16.0% of the adult insured population received at least 1 benzodiazepine annually, 42.9% were older than 50, 55.9% were women, and 5.4% were HDUs. We found that 32.6% were short-term users, 49.0% intermediate and 18.4% continuous. Compared to diazepam, hypnotics had higher risks for high-dose use in at least 1 age group at first-benzodiazepine intake, the risks being greater in elderly subjects and women, the highest risks being with triazolam (adjusted odds ratio = 215.85; 95% confidence interval = 133.75-348.35) in the 69- to 105-year-old group at first-benzodiazepine intake. Anxiolytics had a low risk except for alprazolam and prazepam in the 69- to 105-year-old group at first-benzodiazepine intake, clonazepam and clobazam had the lowest risk in 18- to 43-year-olds at first-benzodiazepine intake. Alprazolam had dispensed volumes increased by threefold over the 12-year period. CONCLUSION: All hypnotics had higher risks for high-dose use compared to diazepam in continuous users. Two anxiolytics, clonazepam and clobazam, had the lowest risks. Hypnotics and the triazolobenzodiazepines alprazolam and triazolam were most problematic. Elderly subjects and women are at greater risks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it