Abstract 18936: Can Biomarkers Improve Risk Stratification of Atrial Fibrillation Patients? Analysis of 3578 Aspirin-treated Patients in ACTIVE and AVERROES
Bibliographic record
Abstract
Oral anticoagulants (OAC) reduce thromboembolism (TE) in atrial fibrillation (AF) but increase the risk of bleeding. Current approaches to risk stratification emphasize high sensitivity for stroke (>90%) but results in poor specificity (<20%), potentially exposing some patients unnecessarily to an increased risk of bleeding. NT-proBNP, D-dimer, and troponin predict stroke in anticoagulated AF patients, but it is unknown whether they can improve the specificity of selecting patients for OAC therapy. We analyzed rates of non-hemorrhagic stroke or systemic embolism according to baseline NT-proBNP, D-dimer, and troponin in 3578 aspirin-treated patients with CHA2DS2-VASc ≥1. Using the 1st quartile as a cut-off, we calculated hazard ratios (HR) of high vs low biomarker levels, adjusting for CHA2DS2-VASc factors. We used a Cox model to assess TE risk prediction with CHA2DS2-VASc compared with CHA2DS2-VASc plus biomarkers. Based on efficacy and safety data from AVERROES, we estimated the net clinical impact (addi...
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".