A double blind, randomized, placebo-controlled trial of quetiapine as an add-on therapy to lithium or divalproex for the treatment of bipolar mania
Bibliographic record
Abstract
The aim of this study was to evaluate the efficacy and tolerability of quetiapine combined with lithium or divalproex in the treatment of bipolar mania. Patients were randomized to 6 weeks of quetiapine (up to 800 mg/day) and lithium/divalproex (Li/DVP) (target trough serum concentrations of 0.7-1.0 mEq/L and 50-100 microg/mL, respectively) or placebo and lithium/divalproex. Quetiapine+lithium/divalproex treatment (n=104) showed a 2.0-point greater improvement on the primary outcome (change from baseline in Young Mania Rating Scale total score at day 21) compared with placebo+lithium/divalproex (n=96), and a 2.8-point greater difference by day 42, but the differences between groups were not statistically significant. Other efficacy measures, however, did show a statistically significant advantage in favor of quetiapine+lithium/divalproex over lithium/divalproex monotherapy at day 42. Improvement of mean Young Mania Rating Scale scores with quetiapine+lithium/divalproex was numerically but not statistically significantly greater than lithium/divalproex monotherapy in the treatment of bipolar mania. Potential reasons for the failure of quetiapine+lithium/divalproex to differentiate from placebo+lithium/divalproex treatment on the primary outcome measure and the implications of this for the treatment of mania and future studies are discussed. Overall, the combination of quetiapine with lithium or divalproex was well tolerated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".