Data analysis strategies for the Accelerating Medicines Partnership® Schizophrenia Program
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Bibliographic record
Abstract
The Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ) project assesses a large sample of individuals at clinical high-risk for developing psychosis (CHR) and community controls. Subjects are enrolled in 43 sites across 5 continents. The assessments include domains similar to those acquired in previous CHR studies along with novel domains that are collected longitudinally across a period of 2 years. In parallel with the data acquisition, multidisciplinary teams of experts have been working to formulate the data analysis strategy for the AMP SCZ project. Here, we describe the key principles for the data analysis. The primary AMP SCZ analysis aim is to use baseline clinical assessments and multimodal biomarkers to predict clinical endpoints of CHR individuals. These endpoints are defined for the AMP SCZ study as transition to psychosis (i.e., conversion), remission from CHR syndrome, and persistent CHR syndrome (non-conversion/non-remission) obtained at one year and two years after baseline assessment. The secondary aim is to use longitudinal clinical assessments and multimodal biomarkers from all time points to identify clinical trajectories that differentiate subgroups of CHR individuals. The design of the analysis plan is informed by reviewing legacy data and the analytic approaches from similar international CHR studies. In addition, we consider properties of the newly acquired data that are distinct from the available legacy data. Legacy data are used to assist analysis pipeline building, perform benchmark experiments, quantify clinical concepts and to make design decisions meant to overcome the challenges encountered in previous studies. We present the analytic design of the AMP SCZ project, mitigation strategies to address challenges related to the analysis plan, provide rationales for key decisions, and present examples of how the legacy data have been used to support design decisions for the analysis of the multimodal and longitudinal data. Watch Prof. Ofer Pasternak discuss his work and this article: https://vimeo.com/1023394132?share=copy#t=0 .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it