Quetiapine: Mood Stabilization Across all Phases of Bipolar Disorder
Bibliographic record
Abstract
Background: Following resolution of acute symptoms (manic and depressed), most patients with bipolar disorder experience affective recurrences and residual functional impairment. This indicates a need for a treatment that not only manages the acute phase of illness but also maintains the stabilized patient. Methods: Data are presented to support the effectiveness of quetiapine in: managing the acutely ill patient; maintaining the euthymic patient by preventing mood event recurrence; and improving patient-perceived functioning. Results: Quetiapine monotherapy demonstrates efficacy against mania symptoms as early as Day 4 (Trials 104 and 105 [pooled datasets]; P< 0.05 vs placebo) and Week 1 in bipolar depression (BOLDER and EMBOLDEN [pooled datasets]; P< 0.001 vs placebo). The acute antidepressive effect was significant for both bipolar I and II populations. As continuation therapy in bipolar depression, quetiapine is associated with a significant reduction in the risk of depressive mood event recurrence (EMBOLDEN I and II; P< 0.001 vs placebo). Moreover, quetiapine significantly reduces the risk of recurrence of mania or depression events as an adjunct to lithium or divalproex (Trials 126 and 127; P< 0.0001), and as monotherapy (Trial 144; P< 0.001). Quetiapine is generally well tolerated in all phases. Conclusions: Quetiapine fulfills mood stabilization criteria by demonstrating efficacy in all phases of bipolar disorder. To date, quetiapine is the only treatment to show robust efficacy against both poles of bipolar disorder, without causing an excess shift to the opposite pole, and to prevent mood episode recurrence irrespective of the index episode.Supported by funding from AstraZeneca Pharmaceuticals LP.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".