Open-Label Adjunctive Topiramate in the Treatment of Unstable Bipolar Disorder
Bibliographic record
Abstract
OBJECTIVE: To assess open-label adjunctive topiramate in the treatment of outpatients with unstable bipolar disorder (BD). METHOD: Outpatients with DSM-IV-defined BD (I or II) exhibiting mood instability were enrolled in this 16-week, open-label, multicentre study. Topiramate was added to existing mood stabilizers and other psychotropic treatments. The primary effectiveness measure was the Clinical Global Impression of Severity (CGI-S) scale; other scales included the Young Mania Rating Scale (YMRS) and the Montgomery-Asberg Depression Rating Scale (MADRS). Safety assessments included monitoring adverse events, measuring tremor, monitoring vital signs and weight, and laboratory indices. We also evaluated patient satisfaction with treatment. RESULTS: A total of 109 patients were enrolled. Intent-to-treat analysis showed significant improvement from baseline in the CGI-S, YMRS, and MADRS, starting at Week 2 (P < 0.001), with further accrual of benefit between Week 2 and Week 16 (P < 0.001). The mean modal dosage of topiramate during the stable dosing period was 180 mg daily. There was a mean 1.8 kg decrease in patient weight from topiramate initiation to Week 16 (P < 0.001). Topiramate was well tolerated by most patients; 11% withdrew from the study owing to adverse events. We noted a significant reduction in the mean severity score for preexisting tremor by Week 8 of treatment (P < 0.005); no notable changes in vital signs were observed. At Week 16, 50% of the patients were "completely satisfied" with topiramate treatment. CONCLUSIONS: Adjunctive topiramate treatment can reduce the severity of manic and depressive symptoms, as well as reducing tremor and weight in outpatients with BD I or II.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".