Real-world effectiveness of aripiprazole once-monthly REACT study: Pooled analysis of two noninterventional studies
Bibliographic record
Abstract
BACKGROUND: Noninterventional naturalistic studies are an important complement to randomized controlled trials. Aripiprazole once-monthly (AOM) is an atypical antipsychotic in a long-acting injectable formulation. METHODS: A pooled analysis of two noninterventional studies was undertaken to validate previous results on AOM effectiveness and safety in a larger population and improve statistical power for preplanned subgroup analyses. We analyzed data from 409 patients with schizophrenia who were treated with AOM and were enrolled in noninterventional studies in Germany (via noninterventional studies registry 15,960 N) and Canada (NCT02131415). Data collected at baseline, 3 and 6 months were analyzed. Among the endpoints were psychopathology (brief psychiatric rating scale [BPRS]) and disease severity (clinical global impression [CGI]). RESULTS: < 0.001). A total of 54.4% were responders (at least 20% reduction) on the BPRS, and 56.5% had a CGI-S-score that was at least 1 level better than baseline. A total of 43.4% were considered responders on both the BPRS and CGI scales. A total of 45.2% were considered in remission. Adverse events were rare and corresponded to the previously known safety profile of AOM. CONCLUSIONS: Treatment with AOM for patients with schizophrenia appeared effective and safe under real-life conditions.
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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 | 0.040 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.029 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".