Individualized pretest risk estimates to guide treatment decisions in patients with clinical high risk for psychotic disorders
Bibliographic record
Abstract
INTRODUCTION: Clinical high risk for psychosis (CHR) states are associated with an increased risk of transition to psychosis. However, the predictive value of CHR screening interviews is dependent on pretest risk enrichment in referred patients. This poses a major obstacle to CHR outreach campaigns since they invariably lead to risk dilution through enhanced awareness. A potential compensatory strategy is to use estimates of individual pretest risk as a 'gatekeeper' for specialized assessment. We aimed to test a risk stratification model previously developed in London, UK (OASIS) and to train a new predictive model for the Swiss population. METHOD: The sample was composed of 513 individuals referred for CHR assessment from six Swiss early psychosis detection services. Sociodemographic variables available at referral were used as predictors whereas the outcome variable was transition to psychosis. RESULTS: Replication of the risk stratification model developed in OASIS resulted in poor performance (Harrel's c=0.51). Retraining resulted in moderate discrimination (Harrel's c=0.67) which significantly differentiated between different risk groups. The lowest risk group had a cumulative transition incidence of 6.4% (CI: 0-23.1%) over two years. CONCLUSION: Failure to replicate the OASIS risk stratification model might reflect differences in the public health care systems and referral structures between Switzerland and London. Retraining resulted in a model with adequate discrimination performance. The developed model in combination with CHR assessment result, might be useful for identifying individuals with high pretest risk, who might benefit most from specialized intervention.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.005 | 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".