Are We Treating Schizophrenia Effectively? Understanding the Primary Outcomes of the CATIE Study
Bibliographic record
Abstract
The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study for schizophrenia was designed to independently evaluate the effectiveness of antipsychotic treatment in "real-world" patients. To assess the effectiveness of the conventional antipsychotics compared to the atypicals as well as the differences among the atypicals, patients were randomized to one of four atypical antipsychotics (olanzapine, quetiapine, risperidone, ziprasidone) or a representative conventional antipsychotic (perphenazine). Effectiveness was defined by time to discontinuation and duration of successful treatment. Time to "all-cause" discontinuation reflects both efficacy (ability of a drug to reduce symptoms) and safety/tolerability. Phase I revealed discontinuation rates ranging from 64% for olanzapine to 82% for quetiapine. Differences among the medications may be important in the selection of a drug for a particular patient. Physicians should involve the patient in choosing their medication by inquiring about the patient's past experience with medications and side effects, educating the patient on the risk-benefit ratio, and considering the patient's preference. To demonstrate how results of the CATIE study can contribute to the knowledge of practicing clinicians, this monograph presents a representative clinical case patient and illustrates how the CATIE safety and efficacy data has important implications for the patient.
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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 | 0.092 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".