Can Aripiprazole Worsen Psychosis in Schizophrenia?
Bibliographic record
Abstract
BACKGROUND: Numerous case reports have reported psychotic worsening when switching to or adding aripiprazole in patients with schizophrenia. The risk of psychotic worsening related to aripiprazole was evaluated through a systematic review and meta-analysis. DATA SOURCES: MEDLINE, Embase, and Cochrane Central Register of Controlled Trials were systematically searched using the following keywords: (schizophr* or schizoaff*) AND aripiprazole, with a limitation of randomized controlled trial and English language (last search: September 9, 2016) by the authors in an independent fashion. STUDY SELECTION: Double-blind, randomized, controlled trials involving a switch to or addition of aripiprazole in schizophrenia spectrum disorders were selected by the authors in an independent fashion. A total of 22 studies (13 switching and 9 adding studies) involving 5,769 patients that met eligibility criteria were identified and included in the meta-analysis. DATA EXTRACTION: Number of patients who experienced psychotic worsening, agitation, or anxiety as well as those who discontinued the study due to all causes, lack of efficacy, or adverse events were extracted. RESULTS: Psychotic worsening was reported as an adverse event in all studies. No significant difference in the risk of psychotic worsening was found between switching to aripiprazole and switching to another antipsychotic (RR = 1.17, 95% CI = 0.97-1.42, P = .10); however, switching to aripiprazole was related to a significantly greater risk of study discontinuation due to lack of efficacy (RR = 1.46, 95% CI = 1.10-1.93, P = .009). Lack of data resulted in no conclusive results as to clinical risks of adding aripiprazole. CONCLUSIONS: Findings suggest that there is no direct evidence that a switch to aripiprazole is related to risk of psychotic worsening in participants in clinical trials, although a switch to aripiprazole may be associated with a higher risk of study discontinuation due to lack of efficacy.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".