Improvement of functioning in patients with schizophrenia: real-world effectiveness of aripiprazole once-monthly (REACT study)
Bibliographic record
Abstract
Abstract Background Functional impairment affects many patients with schizophrenia. Treatment with the long-acting injectable antipsychotic aripiprazole once-monthly (AOM) may help improve functioning. Objectives To explore changes in functioning in patients with schizophrenia who received AOM treatment in observational studies. Methods Here we report functional outcomes in the form of Global Assessment of Functioning (GAF) scores in a pooled analysis of data from two observational studies from Canada (NCT02131415) and Germany (vfa non-interventional studies registry 15960N). Data from 396 patients were analyzed. Results At baseline, the mean GAF score was 47.7 (SD 13.4). During 6 months of treatment with AOM, the mean GAF score increased to 59.4 (SD 15.8). Subgroups stratified by patient age (≤35 years/>35 years), sex, disease duration (≤5 years/>5 years) and disease severity at baseline had all significantly improved their GAF at month 6. 51.5% of the patients showed a GAF score increase of at least 10 points, which was regarded as clinically meaningful, and were considered responders. Conclusions These data show that treatment with AOM may help improve patient functioning in a routine treatment setting. Trial registration NCT02131415 (May 6, 2014), vfa non-interventional studies registry 15960N.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".