A narrative review of strategies for discontinuing long-term benzodiazepine use and methodological recommendations: Is a success rate of only one in three patients sufficient?
Bibliographic record
Abstract
Benzodiazepines (BZs) are psychotropic medications mainly prescribed for insomnia and anxiety. They can cause dependence, leading to decades of use. As such, there is debate grounded in confusion between substance abuse and fear of dependence in some patients. Clinical practice and evidence-based reviews agree that BZ dependence is difficult to treat: without support, only 7% of misusers manage to stop taking them. Analyzing randomized control trials (RCTs), this review examines three main interventions for BZ withdrawal: brief intervention (BI), substitution medication (SM) and cognitive-behavioral therapy (CBT). Post-intervention abstinence rates suggest that BIs can be compared to a simple taper program (TP), requiring low patient involvement, and may enable one in three patients to discontinue BZ use. However, this strategy should be considered with caution: outcomes could be adversely affected by the presence of a psychiatric disorder, a factor not controlled in these studies, nor are long-term results evaluated. Furthermore, can we consider that treating one in three patients is sufficient? CBT proved highly effective, enabling three in four patients attempting to abstain to successfully discontinue use, including patients with insomnia or anxiety. The SM approach showed no superiority over placebo effects. Moreover, abstinence rates being only measured over the very short term, no recommendations can be made regarding their use. This review concludes that there is a major methodological discrepancy between these approaches, BI and SM studies presenting substantially lower methodological quality in comparison to CBT studies. The present article proposes methodological recommendations for the study of BZ withdrawal methods.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".