Antipsychotics and Schizophrenia: From Efficacy and Effectiveness to Clinical Decision-Making
Bibliographic record
Abstract
OBJECTIVE: To comprehensively review the 2 recent and large antipsychotic effectiveness trials for treatment of schizophrenia: the United Kingdom's Cost Utility of the Latest Antipsychotic Drugs in Schizophrenia Study (CUtLASS), and the National Institute of Mental Health-initiated Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial. METHOD: We present a review of the rationale, methodology, and findings to date from the CUtLASS and CATIE schizophrenia trials, including all primary and secondary outcomes. RESULTS: The primary findings from both trials, CUtLASS and CATIE, suggest that first-generation antipsychotics (FGAs) and second-generation antipsychotics (SGAs) are equally effective in the treatment of schizophrenia. The exception is in treatment-resistant populations where clozapine exhibits superiority, compared with other SGAs. In the CATIE trial, there is a suggestion that olanzapine is superior in effectiveness, compared with other nonclozapine SGAs, although this seems to be mediated by past history of olanzapine use, and carries with it increased weight gain and metabolic adverse events. From a cost-effectiveness perspective, there is no evidence that SGAs are superior to FGAs, with findings suggesting the possibility that FGAs may be superior. CONCLUSION: Past efficacy trials have strongly supported the position that SGAs are superior to FGAs in the treatment of schizophrenia and in side effect profile. Two large independent effectiveness trials, CUtLASS and CATIE, have offered a strong challenge to these claims. Both suggest that SGAs, except clozapine in the treatment-resistant population, offer little, if any, clinical benefits, and, moreover, harbour their own significant side effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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".