Efficacy and tolerability of quetiapine in patients with schizophrenia switched from other antipsychotics
Bibliographic record
Abstract
OBJECTIVE The Seroquel Patient Evaluation on Changing Treatment Relative to Usual Medication (SPECTRUM) study assessed the efficacy and tolerability of quetiapine (Seroquel™) in patients with schizophrenia switched from treatments providing suboptimal outcomes. METHODS This was an international, open-label, non-comparative study, designed with titration to 400 mg/day quetiapine over 7 days, then flexible dosing (300-750 mg/day) for 11 weeks. Efficacy was assessed with the Positive and Negative Syndrome Scale (PANSS); Clinical Global Impression (CGI) Severity of Illness and Global Improvement scores; and the Calgary Depression Scale for Schizophrenia (CDSS). Clinical benefit and tolerability were also assessed. RESULTS The mean modal dose of quetiapine was 505 mg/day; 509 patients switched to quetiapine from olanzapine (13%), risperidone (11%), conventional antipsychotics (37%) and combinations of antipsychotics (28%), amongst others. Significant decreases in CGI Severity of Illness and PANSS scores and a significant improvement in CDSS score resulted from the switch (all P<0.001 versus baseline). There were significant reductions in extrapyramidal symptoms (EPS) on the Simpson-Angus Scale (SAS) and Barnes Akathisia Scale (BAS) (both P<0.001 versus baseline) and a low incidence of EPS-related adverse events (4.7%). CONCLUSION Results indicate that switching to quetiapine was clinically beneficial for patients with poor efficacy or intolerable side effects on their previous antipsychotic medication.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".