AI-Based prediction of depression symptomatology in first episode psychosis patients: insights from the EUFEST and RAISE-ETP clinical trials
Bibliographic record
Abstract
This registration outlines a study that aims to develop machine learning models that predict depression symtomatology in first episode psychosis (FEP) patients using data from two multi-site clinical trials, the European First Episode Schizophrenia Trial (EUFEST) and the Recovery After an Initial Schizophrenia Episode early treatment program (RAISE-ETP). The study will focus on incorporating clinical, cognitive, sociodemographic, quality of life and general health variables to train and test support vector machine classifiers and regressors in predicting depression symptomatology change based on the Calgary Depression Scale for Schizophrenia (CDSS), administered at baseline and several follow ups in both clinical trials. The primary objective of the study is to assess the predictive accuracy of these models for early detection of depression symptom progression in FEP patients undergoing antipsychotic treatment. Additionally, we will aim to predict post-schizophrenic depression epidodes using the same baseline data. Secondary objectives include identifying key predictors of future depression symptomatology and evaluating the clinical utility of the models in predicting depression symptomatology and providing tailored treatment plans.
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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 | 0.031 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".