Off-Label Use of Medications for Treatment of Benzodiazepine Use Disorder
Bibliographic record
Abstract
There is a high rate of benzodiazepine use in the population. Benzodiazepines are used for multiple indications (anxiety, seizures, alcohol withdrawal, muscular relaxation and anesthesia). Benzodiazepines are also addictive substances and a non-negligible fraction of regular users will develop dependence. There is currently no approved pharmacotherapy for benzodiazepine use disorder treatment and optimal strategies for treatment are unclear. In this review, we aimed to summarize the findings on off-label pharmacologic therapy that have been used for BZD dependence. One classical approach is to provide a slow taper associated with counseling. Anti-epileptic drugs appear also to alleviate symptoms of withdrawal. The long-term strategies of maintenance therapy (with benzodiazepine) or of blocking therapy (with a GABA antagonist such as flumazenil) could provide some clinical benefit but have not yet been tested appropriately. Pregabalin appears promising and deserves further investigation. There is a clear need for more clinical trials in this area to improve care.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".