Cariprazine in the acute treatment of unipolar and bipolar depression: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Cariprazine has emerged as a promising augmenting treatment agent for unipolar depression and as a monotherapy option for bipolar depression. We evaluated cariprazine's efficacy in treating acute major depressive episodes in individuals with major depressive disorder (MDD) or bipolar disorder. METHODS: A systematic review was conducted on MEDLINE, Embase, PsycINFO, Scopus and Web of Science, ClinicalTrials.gov and ScanMedicine. Study quality was assessed using the RoB 2 tool. Pairwise and dose-response meta-analyses were conducted with RStudio. Evidence quality was assessed with GRADE. RESULTS: Nine RCTs meeting inclusion criteria encompassed 4889 participants. Cariprazine, compared to placebo, significantly reduced the MADRS score (MD = -1.49, 95 % CI: -2.22 to -0.76) and demonstrated significantly higher response (RR = 1.21, 95 % CI: 1.12 to 1.30) and remission (RR = 1.19, 95 % CI: 1.06 to 1.34) rates. Subgroup analysis unveiled statistically significant reductions in MADRS score in MDD (MD = -1.15, 95 % CI: -2.04 to -0.26) and bipolar I disorder (BDI) (MD = -2.53, 95 % CI: -3.61 to -1.45), higher response rates for both MDD (RR = 1.19, 95 % CI: 1.08 to 1.31) and BDI (RR = 1.27, 95 % CI: 1.10 to 1.46), and higher remission rates only for BDI (RR = 1.41, 95 % CI: 1.24 to 1.60). A higher rate of treatment discontinuation due to adverse events was observed. LIMITATIONS: Reliance solely on RCTs limits generalisability; strict criteria might not reflect real-world diversity. CONCLUSIONS: Cariprazine demonstrates efficacy in treating major depressive episodes, although variations exist between MDD and BDI and tolerability may be an issue.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".