Topiramate for cocaine dependence: a systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
AIMS: To assess the efficacy of topiramate in treating cocaine use disorder (i.e. retention, efficacy, safety and craving reduction) through a systematic review and meta-analysis. METHODS: We searched six scientific databases from inception to 23 December 2014 with no date limits. Data were reviewed, extracted and analysed systematically. Studies were included if they were peer-reviewed randomized control trials with participants meeting diagnostic criteria for cocaine dependence or cocaine use disorder, with the treatment arm involving topiramate with or without psychosocial intervention, and the control arm involving no intervention or psychosocial intervention with or without placebo. A random-effects meta-analytical model was computed. RESULTS: Five studies met inclusion criteria (n = 518). Topiramate was compared with placebo (four studies) and no medication (one study). In a meta-analysis, we observed no significant differences between topiramate and placebo in improving treatment retention risk ratio (RR) = 0.85; 95% confidence interval (CI) = 0.60-1.22, P = 0.38. However, compared with a placebo, use of topiramate was associated with increased continuous abstinence in two of five studies (RR = 2.43; 95% CI = 1.31-4.53, P = 0.005). No differences were observed in frequency of adverse effects reported between topiramate and placebo (RR = 1.06; 95% CI = 0.91-1.23, P = 0.48). Topiramate was associated significantly (P < 0.05) with a reduction in craving in only one of five studies. CONCLUSIONS: Evidence does not currently support the use of topiramate to improve treatment retention for cocaine use disorder, although it may extend cocaine abstinence with a similar risk of adverse events compared with placebo.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".