Efficacy of cariprazine in bipolar I depression across patient characteristics: a post hoc analysis of pooled randomized, placebo-controlled studies
Bibliographic record
Abstract
Patients who experience bipolar depression have diverse demographic and clinical characteristics that have the potential to impact treatment. The efficacy of cariprazine in bipolar I depression was evaluated in patient subgroups defined by baseline demographic and clinical characteristics. Post hoc analyses of data from three randomized, double-blind, placebo-controlled trials in bipolar I depression (NCT01396447, NCT02670538 and NCT02670551) evaluated mean change from baseline in Montgomery-Åsberg Depression Rating Scale (MADRS) total scores for pooled cariprazine 1.5-3 mg/d versus placebo in subgroups defined by demographic and clinical characteristics. The least-squares mean difference in MADRS total score change from baseline was statistically significant for cariprazine 1.5-3 mg/d versus placebo in all patient subgroups analyzed (P < 0.05 all subgroups): demographic characteristics (age, sex, white or black race and obese/nonobese BMI); episode characteristics (defined by current episode duration, number of previous manic/mixed and depressive episodes, and prior bipolar disorder medication use) and disease severity (groups above and below Clinical Global Impressions-Severity and MADRS cutoff scores). Cariprazine 1.5-3 mg/d consistently improved depressive symptoms in all patient subgroups without regard to differences in baseline demographic and clinical characteristics, suggesting broad efficacy across a spectrum of patients with bipolar I depression.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".