A 12-Week Randomized, Double-Blind, Placebo-Controlled Clinical Trial of Topiramate for the Treatment of Compulsive Buying Disorder
Bibliographic record
Abstract
BACKGROUND: Topiramate is an anticonvulsant that has shown promise as a pharmacological agent for the treatment of addictive disorders, including compulsive buying disorder (CBD). The aim of the present study was to examine the efficacy of topiramate in the treatment of CBD and its associated characteristics using a 12-week randomized, double-blind, placebo-controlled design. METHODS: Fifty patients seeking treatment of CBD who met the inclusion criteria were randomly assigned to either the experimental group (n = 25) or the control group (n = 25). Both groups received 4 sessions of psychoeducation. RESULTS: Forty-four participants completed the follow-up with no differences in the rate of dropout between groups. There were no differences between participants who received topiramate or placebo in reducing CBD symptoms assessed by the primary outcome scale (Yale-Brown Obsessive-Compulsive Scale - Shopping Version). However, participants who received topiramate were significantly more likely to show clinical improvement when assessed by a secondary outcome measure, the Compulsive Buying Follow-Up Scale. In addition, there was a trend among participants who received topiramate to report improvements in aspects of hoarding and impulsivity compared with the control group. There were significant improvements in comorbid depression and social adjustments over time, but no group × time interaction was found. CONCLUSIONS: The results do not provide support for the use of topiramate in the treatment of CBD. Future investigation with larger and representative samples and longer follow-up period are needed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".