The Early Psychosis Screener for Internet (EPSI)-SR: Predicting 12 month psychotic conversion using machine learning
Bibliographic record
Abstract
INTRODUCTION: A faster and more accurate self-report screener for early psychosis is needed to promote early identification and intervention. METHODS: Self-report Likert-scale survey items were administered to individuals being screened with the Structured Interview for Psychosis-risk Syndromes (SIPS) and followed at eight early psychosis clinics. An a priori analytic plan included Spectral Clustering Analysis to reduce the item pool, followed by development of Support Vector Machine (SVM) classifiers. RESULTS: The cross-validated positive predictive value (PPV) of the EPSI at the default cut-off (76.5%) exceeded that of the clinician-administered SIPS (68.5%) at separating individuals who would not convert to psychosis within 12 months from those who either would convert within 12 months or who had already experienced a first episode psychosis (FEP). When used in tandem with the SIPS on clinical high risk participants, the EPSI increased the combined PPV to 86.6%. The SVM classified as FEP/converters only 1% of individuals in non-clinical and 4% of clinical low risk populations. Sensitivity of the EPSI, however, was 51% at the default cut-off. DISCUSSION: The EPSI identifies, comparably to the SIPS but in less time and with fewer resources, individuals who are either at very high risk to develop a psychotic disorder within 12 months or who are already psychotic. At its default cut-off, EPSI misses 49% of current or future psychotic cases. The cut-off can, however, be adjusted based on purpose. The EPSI is the first validated assessment to predict 12-month psychotic conversion. An online screening system, www.eps.telesage.org, is under development.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".