Efficacy and safety of topiramate in binge eating disorder: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: To assess the efficacy and safety of topiramate in treating binge eating disorder (BED), using a systematic review and meta-analysis of the available randomized clinical trials (RCTs). METHODS: The RCTs assessing topiramate vs placebo with or without adjunctive psychotherapy in BED were reviewed using a systematic search in the PubMed, Web of Science, PsycINFO, Cochrane Database of Systematic Review, and ClinicalTrials.gov search Websites, from inception to November 2019. Main outcomes were the changes in binge frequency, quality of life, and weight, respectively. Effect estimates were pooled using random-effect models and presented as risk ratios (RRs) or mean differences (MDs) and their 95% confidence interval (95% CI). Data extraction was performed by two independent reviewers. RESULTS: Three studies were eligible for inclusion, involving 528 BED patients. Topiramate was found to be significantly more efficacious than placebo in reducing: (a) the number of binge episodes per week (MD = -1.31; 95% CI = -2.58 to -0.03; I2 = 94%); (b) the number of binge days per week (MD = -0.98; 95% CI = -1.80 to -0.16; I2 = 94%); and (c) weight (MD = -4.91 kg; 95% CI = -6.42 to -3.41; I2 = 10%). However, participants in the topiramate groups withdrew significantly more frequently for safety reasons, relative to placebo participants (RR = 1.90; 95% CI = 1.13-3.18, I2 = 0%). CONCLUSIONS: Preliminary findings support a possible efficacy of topiramate for the treatment of BED, even if safety concerns could limit the practical use of this treatment in BED subjects.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".