P822Predictive value of a biomarker panel for coronary plaque morphology in patients with stable coronary artery disease
Bibliographic record
Abstract
Background: Risk stratification in patients with coronary artery disease (CAD) is crucial to identify patients at risk for future events. Recently, the development of proximity extension assays (PEA) has enabled simultaneous measurement of large amounts of proteins, paving the way for the use of proteomics in large clinical populations. Purpose: To investigate the ability of targeted proteomics to identify coronary computed tomography angiography (CCTA)-derived coronary plaque morphology in patients with suspected CAD. Methods: A total of 196 patients with suspected CAD underwent CCTA. Subsequently EDTA plasma was stored for further analysis. CCTA's were analyzed for the presence of coronary high-risk lesions, defined by the presence of ≥2 adverse plaque characteristics (positive remodeling, low attenuation plaque, spotty calcification and/or napkin ring sign). Plasma levels of 359 proteins were evaluated with the use of PEA and were used to generate deep learning models for high-risk coronary lesions and for the absence of CAD. The performance of these machine learning models was tested against traditional risk prediction with the Framingham risk model (refit Framingham) and Framingham risk model with additional clinical variables (refit Framingham plus), refit to the cohort of this study.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".