Predictors of Long-Term Benzodiazepine Abstinence in Participants of a Randomized Controlled Benzodiazepine Withdrawal Program
Bibliographic record
Abstract
OBJECTIVE: To identify predictors of resumed benzodiazepine use after participation in a benzodiazepine discontinuation trial. METHOD: We performed multiple Cox regression analyses to predict the long-term outcome of a 3-condition, randomized, controlled benzodiazepine discontinuation trial in general practice. RESULTS: Of 180 patients, we completed follow-up for 170 (94%). Of these, 50 (29%) achieved long-term success, defined as no use of benzodiazepines during follow-up. Independent predictors of success were as follows: offering a taper-off program with group therapy (hazard ratio [HR] 2.4; 95% confidence interval [CI], 1.5 to 3.9) or without group therapy (HR 2.9; 95% CI, 1.8 to 4.8); a lower daily benzodiazepine dosage at the start of tapering off (HR 1.5; 95% CI, 1.2 to 1.9); a substantial dosage reduction by patients themselves just before the start of tapering off (HR 2.1; 95% CI, 1.4 to 3.3); less severe benzodiazepine dependence, as measured by the Benzodiazepine Dependence Self-Report Questionnaire Lack of Compliance subscale (HR 2.4; 95%CI, 1.1 to 5.2); and no use of alcohol (HR 1.7; 95% CI, 1.2 to 2.5). Patients who used over 10 mg of diazepam equivalent, who had a score of 3 or more on the Lack of Compliance subscale, or who drank more than 2 units of alcohol daily failed to achieve long-term abstinence. CONCLUSIONS: Benzodiazepine dependence severity affects long-term taper outcome independent of treatment modality, benzodiazepine dosage, psychopathology, and personality characteristics. An identifiable subgroup needs referral to specialized 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".