Selection of Atypical Antipsychotics for the Management of Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To review the evidence for selecting one atypical antipsychotic agent over another for management of schizophrenia. DATA SOURCES: A literature search of MEDLINE (1966-June 2003), EMBASE (1998-June 2003), and the Cochrane Library was conducted using the following terms: schizophrenia, quetiapine, ziprasidone, olanzapine, aripiprazole, and risperidone. Bibliographies of relevant articles were hand-searched for additional references. STUDY SELECTION AND DATA EXTRACTION: Prospective, randomized, blinded trials and meta-analyses that directly or indirectly compared > or =2 atypical antipsychotic agents in the management of schizophrenia are included in this review. Studies comparing an atypical agent with clozapine are not included. DATA SYNTHESIS: A small number of prospective, randomized, blinded trials that compare efficacy and tolerability of olanzapine and risperidone have been published. These trials did not reveal clinically meaningful differences in efficacy but did confirm that their adverse effect profiles are slightly different (more weight gain with olanzapine and more extrapyramidal reactions with risperidone). Direct comparisons between other atypical antipsychotics are not available. Systematic reviews (indirect comparisons) of placebo-controlled or traditional antipsychotic-controlled trials suggest similar efficacy for quetiapine, olanzapine, and risperidone when placebo is the comparator and inferior efficacy of quetiapine compared to olanzapine and risperidone when haloperidol is the comparator. The few available economic analyses are difficult to interpret in light of current practice. CONCLUSIONS: Additional randomized, blinded clinical trials directly comparing efficacy, tolerability, and cost-effectiveness are needed to confirm the proposed differences among atypical antipsychotic agents before recommendations can be made with confidence.
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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 | 0.018 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".