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 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".