Clinical Utility of Early Intervention Including the 5-Step Precision Medicine Method in First-Episode Psychosis: Protocol for a Cohort Study With Nested Economic and Process Evaluations
Bibliographic record
Abstract
BACKGROUND: Psychotic disorders such as schizophrenia present a significant challenge to health care systems due to their high disability rates and treatment costs. With discontinuation rates for antipsychotics reaching over 40% in the first year and 80% after 3 years, it is crucial to tailor antipsychotic selection and dosing early in treatment. Personalized precision psychiatry, underpinned by pharmacogenetics, holds considerable potential in individualizing antipsychotic treatment for patients with first-episode psychosis. An internationally pioneering method called 5-step precision medicine (5SPM) focuses on the application of pharmacogenetics to clinical practice. The recently launched Prevention and Early Intervention in Mental Health (PRINT) program in Salamanca, Spain, integrates this method to enhance early intervention for adolescents and young people with first-episode psychosis. OBJECTIVE: The Clinical Utility of Early Intervention Including the 5SPM Method in First-Episode Psychosis (CLUMP) project aims to explore whether an early intervention model of personalized precision psychiatry including pharmacogenetics improves adherence to antipsychotic medicines and, therefore, clinical and functional outcomes in young people experiencing the first episode of a psychotic illness. METHODS: To achieve our objectives, we shall compare adherence to the first prescribed antipsychotic medication and clinical and functional outcomes between patients with first-episode psychosis. We shall compare 2 cohorts: cohort 1 will receive the recently introduced PRINT program including the 5SPM method, and cohort 2 will have received standard care provided by mental health services before the PRINT program implementation. The primary outcome to measure treatment adherence will be all-cause discontinuation proportions during the 1-year follow-up. Secondary outcome measures will include pragmatic efficacy, tolerability, and functional outcome measures. For additional comparative purposes, we shall analyze the environmental, clinical, and pharmacogenetic information of patients with psychotic disorders of more than 5 years of evolution and with other mental disorders whose data are currently stored and have been ethically approved for research use. A total of 300 patients will be included in the study. Analyses will include descriptive statistics, comparison tests, Kaplan-Meier survival curves, multivariate log rank tests, qualitative analysis, and cost-benefit evaluation. RESULTS: Ethics approval was obtained in June 2023. Recruitment for the CLUMP project began in January 2025, and enrollment for cohort 1 will continue until May 2026. All data collection is expected to be completed by June 2027. Data analyses are estimated to take approximately 6 months. The project is scheduled to conclude in December 2027. CONCLUSIONS: The CLUMP project is set to provide the first clear blueprint for implementing and evaluating the impact of personalized precision psychiatry based on pharmacogenetics in the context of early intervention programs for the benefit of young people experiencing the first episode of a severe mental illness such as schizophrenia. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/74408.
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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 | 0.069 | 0.076 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".