Adjunctive cariprazine for major depressive disorder: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Converging evidence has suggested that treatment augmentation with a second-generation atypical antipsychotic (SGA) may improve treatment outcomes in major depressive disorder (MDD) patients after an incomplete response to a first-line antidepressant. Cariprazine is a recently approved SGA for MDD augmentation. Herein, we evaluate both continuous (ie, change in depressive symptom severity scores over time) and categorical (ie, remission and response rates) outcomes. Following a full-text review, four randomized controlled trials (RCTs) were included in our meta-analysis, while five studies were included for a qualitative review. Risk ratios (RRs) were calculated for all included randomized controlled studies to determine the relative response and remission rates of cariprazine compared to placebo augmentation. The RR for all-cause dropout was also determined as a proxy for overall acceptability. Two studies found a statistically significant treatment response using cariprazine augmentation. One study observed depressive symptom remission for cariprazine compared to placebo. Our random-effects model revealed moderate antidepressant effects of cariprazine, with a standardized mean difference (SMD) in Montgomery–Åsberg Depression Rating Scale (MADRS) scores of −1.79 (95% CI): −2.89, −0.69). Our pooled response RR and remission RR were calculated as 1.21 (95% CI: 1.05, 1.39, P=0.008) and 0.99 (95% CI: 0.84, 1.17, P=0.91), respectively. The RR for response was statistically significant (P<0.05). However, the RR for remission was not statistically significant. The findings from our meta-analysis include a variable magnitude of effects. Evidence suggests cariprazine may be an effective treatment for MDD; however, further results are needed to clarify this relation.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".