Topiramate Use in Obese Patients with Binge Eating Disorder: An Open Study
Bibliographic record
Abstract
OBJECTIVE: To assess topiramate's efficacy and tolerability in a group of obese binge eaters with no neuropsychiatric comorbidity. METHOD: We consecutively selected 8 obese patients with binge eating disorder (BED) and no medical or psychiatric comorbidity from individuals seeking treatment for obesity. Treatment with topiramate at 150 mg daily was administered over a 16-week period. To assess outcome, we employed the days with binge episodes per week (DBE), the Binge Eating Scale (BES), the Beck Depression Inventory (BDI), and body weight evaluation. RESULTS: Of the 6 patients who completed the trial, all showed reduced binge eating. Four patients presented a total remission, and 2 had a marked reduction in binge eating frequency. The mean DBE decreased significantly from 4.3 to 1.1 (P = 0.03), as did the BES scores, which fell from 31.8 to 15.3 (P = 0.04). Moreover, there was a statistically significant weight loss (mean 4.1 kg, P = 0.04). The most frequent side effects were paresthesias, fatigue, and somnolence. CONCLUSION: Topiramate may be an effective and well-tolerated agent in the treatment of BED in obese patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".