Body fluid biomarkers and psychosis risk in The Accelerating Medicines Partnership® Schizophrenia Program: design considerations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Advances in proteomic assay methodologies and genomics have significantly improved our understanding of the blood proteome. Schizophrenia and psychosis risk are linked to polygenic scores for schizophrenia and other mental disorders, as well as to altered blood and saliva levels of biomarkers involved in hormonal signaling, redox balance, and chronic systemic inflammation. The Accelerating Medicines Partnership® Schizophrenia (AMP®SCZ) aims to ascertain biomarkers that both predict clinical outcomes and provide insights into the biological processes driving clinical outcomes in persons meeting CHR criteria. AMP®SCZ will follow almost 2000 CHR and 640 community study participants for two years, assessing biomarkers at baseline and two-month follow-up including the collection of blood and saliva samples. The following provides the rationale and methods for plans to utilize polygenic risk scores for schizophrenia and other disorders, salivary cortisol levels, and a discovery-based proteomic platform for plasma analyses. We also provide details about the standardized methods used to collect and store these biological samples, as well as the study participant metadata and quality control measures related to preanalytical factors that could influence the values of the biomarkers. Finally, we discuss our plans for analyzing the results of blood- and saliva-based biomarkers. Watch Dr. Perkins discuss their work and this article: https://vimeo.com/1062879582?share=copy#t=0 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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