Comparative effectiveness of interventions to facilitate deprescription of benzodiazepines and other sedative hypnotics: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To review evidence from randomised trials assessing the effectiveness of strategies to deprescribe benzodiazepines and closely related sedative hypnotics (BSH). DESIGN: Systematic review and meta-analysis of randomised controlled trials. DATA SOURCES: MEDLINE, Embase, CINAHL, PsycInfo, and CENTRAL, searched from inception to August 2024, and reference lists of included studies and similar systematic reviews. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Eligible studies randomised adults using BSH for insomnia to interventions aimed at deprescribing BSH, strategies to implement these interventions in healthcare settings, or usual care or placebo. METHODS: Reviewers worked independently and in duplicate to screen search results, extract data, and assess risk of bias. Similar interventions were grouped together, frequentist random effects meta-analysis was conducted, and the certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. RESULTS: The review identified 58 publications reporting on 49 unique trials with more than 39 000 patients. Interventions were classified into the following categories: tapering, patient education, physician education, combined patient and physician education, cognitive behavioural therapy, medication review, mindfulness, motivational interviewing, pharmacist led interventions, and drug assisted tapering and withdrawal. Low certainty evidence suggests that education of patients (144 (95% confidence interval 61 to 246) more per 1000 patients), medication review (104 (34 to 191) more), and a pharmacist led educational intervention (491 (234 to 928) more) may increase the proportion of patients who discontinue BSH compared with usual care. Moderate certainty evidence suggests that education of patients probably has little or no effect on physical function, mental health, and signs and symptoms of insomnia. No evidence was found regarding these other outcomes for medication review or for the pharmacist led educational intervention. No compelling evidence was found that other interventions may help patients to discontinue BSH. Moreover, no high or moderate certainty evidence was found that any of the interventions caused an increase in dropouts. Finally, low certainty evidence suggests that multicomponent interventions may be more effective at facilitating discontinuation of BSH than single component interventions. CONCLUSION: The evidence on the effectiveness of interventions to discontinue BSH is of low certainty. Educating patients, doing medication reviews, and a pharmacist led educational intervention may increase the proportion of patients who discontinue BSH.
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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 | 0.027 | 0.070 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.042 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".