Extended-release quetiapine fumarate (quetiapine XR) versus risperidone in the treatment of depressive symptoms in schizophrenic or schizoaffective patients
Bibliographic record
Abstract
Introduction Depressive symptoms are associated with poor outcomes, increased risk of relapse and high suicide rates in patients with schizophrenia and schizoaffective disorder (1, 2). Objective Assess the efficacy of quetiapine XR (QTP-XR) versus risperidone on depressive symptoms in schizophrenia and schizoaffective disorder. Methods A randomised, open-label, parallel-group, flexible-dose study (NCT00640562). Primary endpoint LSM change from baseline to Week 12 in Calgary Depression Scale for Schizophrenia (CDSS) (3) score. Secondary endpoints: change in HAM-D and PANSS scores, and adverse events (AEs). No scorrection for multiplicity was done for the secondary endpoints. Results 216 patients received QTP-XR (n = 109; 400–800 mg/day) or risperidone (n = 107; 4–6 mg/day). From baseline, QTP-XR significantly reduced CDSS, HAM-D and PANSS negative total scores compared with risperidone (QTP-XR vs risperidone: -7.31 versus -5.53, p = 0.0107; -14.68 versus -11.53, p = 0.0005; -8.23 versus -5.45, p = 0.0008, respectively). No major differences in AEs were observed between QTP-XR and risperidone. Four serious AEs were experienced with QTP-XR and 5 with risperidone. Two patients receiving QTP-XR died (unrelated to study drug). Prolactin levels were significantly reduced from baseline with QTP-XR versus risperidone (-9.15 ng/mL and +22.18 ng/mL respectively; p < 0.0001). No important differences were seen in other laboratory parameters. Conclusions In this study, QTP-XR was superior to risperidone at reducing depressive symptoms in schizophrenia or schizoaffective disorder according to CDSS, HAM-D and PANSS negative scores.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".