Topiramate Treatment for SSRI-Induced Weight Gain in Anxiety Disorders
Bibliographic record
Abstract
BACKGROUND: Antidepressants, including selective serotonin reuptake inhibitors (SSRIs), have been associated with significant weight gain, a problem that frequently leads to noncompliance and premature discontinuation of treatment. Topiramate is a novel anticonvulsant that has also been used as a mood stabilizer and augmentation agent in mood disorders. Topiramate has been observed to have an interesting side effect of weight loss in some individuals. In this study, topiramate was added to the treatment regimen of patients with a primary DSM-IV anxiety disorder who had experienced substantial SSRI-induced weight gain, in an attempt to induce weight loss. METHOD: Topiramate was added to SSRI treatment in 15 anxiety disorder patients, starting at a dose of 50 mg/day and titrating up to a target daily dose of 100 mg/day, with a maximum dose of 250 mg/day. Subjects' weight was measured at baseline and after 5 and 10 weeks of treatment. RESULTS: Before topiramate treatment, SSRI-treated subjects in this sample had gained a mean of 13.0 +/- 8.4 kg (28.6 +/- 18.5 lb). After the addition of a mean dose of 135.0 +/- 44.1 mg/day of topiramate for approximately 10 weeks, subjects lost a mean of 4.2 +/- 6.0 kg (9.3 +/- 13.3 lb). CONCLUSION: Topiramate may have a role in managing SSRI-induced weight gain in anxiety disorder patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".