Antipsychotic drugs in first-episode psychosis: a target trial emulation in the FEP-CAUSAL Collaboration
Bibliographic record
Abstract
Good adherence to antipsychotic therapy helps prevent relapses in first-episode psychosis (FEP). We used data from the FEP-CAUSAL Collaboration, an international consortium of observational cohorts, to emulate a target trial comparing antipsychotics, with treatment discontinuation as the primary outcome. Other outcomes included all-cause hospitalization. We benchmarked our results to estimates from the European First Episode Schizophrenia Trial, a randomized trial conducted in the 2000s. We included 1097 patients with a psychotic disorder and less than 2 years since psychosis onset. Inverse-probability weighting was used to control for confounding. The estimated 12-month risks of discontinuation for aripiprazole, first-generation agents, olanzapine, paliperidone, quetiapine, and risperidone were 61.5% (95% CI, 52.5-70.6), 73.5% (95% CI, 60.5-84.9), 76.8% (95% CI, 67.2-85.3), 58.4% (95% CI, 40.4-77.4), 76.5% (95% CI, 62.1-88.5), and 74.4% (95% CI, 67.0-81.2), respectively. Compared with aripiprazole, the 12-month risk differences were -15.3% (95% CI, -30.0 to 0.0) for olanzapine, -12.8% (95% CI, -25.7 to -1.0) for risperidone, and 3.0% (95% CI, -21.5 to 30.8) for paliperidone. The 12-month risks of hospitalization were similar between agents. Our estimates support use of aripiprazole and paliperidone as first-line therapies for FEP. Benchmarking yielded similar results for discontinuation and absolute risks of hospitalization as in the original trial, suggesting that data from the FEP-CAUSAL Collaboration sufficed to remove confounding for these clinical questions. This article is part of a Special Collection on Mental Health.
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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 | 0.153 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".