Accelerating Medicines Partnership<sup>®</sup> Schizophrenia (AMP<sup>®</sup>SCZ): developing tools to enable early intervention in the psychosis high risk state
Bibliographic record
Abstract
Accelerating Medicines Partnership ® Schizophrenia (AMP ® SCZ): developing tools to enable early intervention in the psychosis high risk stateSchizophrenia is a severe mental illness that presents with pos itive, negative and cognitive symptoms and ranks among the top 15 leading causes of disability worldwide 1 .Signs of risk for developing this illness can occur months to years before diagno sis.This early period, referred to as the clinical high risk (CHR) for psychosis state, reflects a time during which attenuated psy chotic symptoms, marked declines in social and role functioning, helpseeking behavior, and nonpsychotic comorbidity are noted.Intervention in the CHR state can prevent future illnessrelated dis ability 2 .Longitudinal studies of CHR individuals show that, at two year followup, approximately 20% transition to psychosis 3 , 41% undergo remission 4 , but many of the remainder experience sig nificant symptoms and problems in functioning 4 .Studies are underway to establish risk calculators and biomarkers that can help identify CHR individuals who are most likely to convert to psychosis, but more work is needed to develop tools that use mechanistic input to stratify CHR populations by predicted clini cal outcomes beyond psychosis 5 .The CHR stage represents a unique opportunity to develop interventions guided by such tools, focused on reducing conversion to psychosis and improv ing longterm functional outcomes.Aimed at capitalizing on this opportunity, the Accelerating
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".