The efficacy of cariprazine on cognition: a post hoc analysis from phase II/III clinical trials in bipolar mania, bipolar depression, and schizophrenia
Bibliographic record
Abstract
Abstract Objective To investigate the effect of cariprazine on cognitive symptom change across bipolar I disorder and schizophrenia. Methods Post hoc analyses of 3- to 8-week pivotal studies in bipolar I depression and mania were conducted; one schizophrenia trial including the Cognitive Drug Research System attention battery was also analyzed. Outcomes of interest: Montgomery-Åsberg Depression Rating Scale [MADRS], Functioning Assessment Short Test [FAST], Positive and Negative Syndrome Scale [PANSS]). LSMDs in change from baseline to end of study were reported in the overall intent-to-treat population and in patient subsets with specified levels of baseline cognitive symptoms or performance. Results In patients with bipolar depression and at least mild cognitive symptoms, LSMDs were statistically significant for cariprazine vs placebo on MADRS item 6 (3 studies; 1.5 mg=−0.5 [P<.001]; 3 mg/d=−0.2 [P<.05]) and on the FAST Cognitive subscale (1 study; 1.5 mg/d=−1.4; P=.0039). In patients with bipolar mania and at least mild cognitive symptoms, the LSMD in PANSS Cognitive subscale score was statistically significant for cariprazine vs placebo (3 studies; −2.1; P=.001). In patients with schizophrenia and high cognitive impairment, improvement in power of attention was observed for cariprazine 3 mg/d vs placebo (P=.0080), but not for cariprazine 6 mg/d; improvement in continuity of attention was observed for cariprazine 3 mg/d (P=.0012) and 6 mg/d (P=.0073). Conclusion These post hoc analyses provide preliminary evidence of greater improvements for cariprazine vs placebo across cognitive measures in patients with bipolar I depression and mania, and schizophrenia, suggesting potential benefits for cariprazine in treating cognitive symptoms.
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".