Validation of the ORIGIN Cardiovascular Biomarker Panel and the Value of Adding Troponin I in Dysglycemic People
Bibliographic record
Abstract
Background: Analyses of stored blood from the Outcomes Reduction with an Initial Glargine Intervention (ORIGIN) trial identified biomarkers that supplemented clinical risk factors for cardiovascular (CV) events or death. Their performance in participants with diabetes in the Heart Outcomes Prevention Evaluation (HOPE) study and the incremental value of adding high-sensitivity assays of serum troponin I (hsTnI) in the ORIGIN study were assessed. Methods: Levels of the 10 ORIGIN biomarkers for the composite CV outcome of myocardial infarction, stroke, or CV death were measured in 350 HOPE study participants with diabetes and stored serum that included all 77 who experienced this outcome. The effect of adding hsTnI levels to this panel, and the previously identified ORIGIN biomarkers for this composite outcome, this outcome, or revascularization or heart failure, and for mortality was also analyzed. Results: Within the HOPE cohort, the ORIGIN biomarker panel increased the C statistic from 0.63 for clinical risk factors alone to 0.67 with the addition of the 10 biomarkers, and the net reclassification improvement was 0.14 (95% confidence interval, 0.01, 0.28). Within the ORIGIN cohort, hsTnI levels predicted all three outcomes during follow-up both alone, and independently of the other biomarkers, which all remained independent predictors of outcomes after inclusion of the hsTnI levels. The hsTnI level interacted with follow-up time such that it was a stronger predictor of earlier vs later events. Conclusion: The ORIGIN biomarkers predicted CV outcomes in the independent HOPE cohort. Adding hsTnI levels to the previously identified models in ORIGIN modestly improved their performance.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".