Evaluating the effects of socioeconomic status on stroke and bleeding risk scores and clinical events in patients on oral anticoagulant for new onset atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: The risk of thromboembolism and bleeding before initiation of oral anticoagulant (OAC) in atrial fibrillation patients is estimated by CHA2DS2-VASc and HAS-BLED scoring system, respectively. Patients' socioeconomic status (SES) could influence these risks, but its impact on the two risk scores' predictive performance with respect to clinical events remains unknown. Our objective was to determine if patient SES defined by area deprivation index (ADI), in conjunction with CHA2DS2-VASc and HAS-BLED scores, could guide oral anticoagulation therapy. METHODS AND FINDINGS: The study cohort included newly diagnosed patients with AF who were treated with warfarin. The cohort was stratified by the time in therapeutic range of INR (TTR), ADI, CHA2DS2-VASc, and HAS-BLED risk scores. TTR and ischemic and bleeding events during the first year of therapy were compared across subpopulations. Among 7274 patients, those living in the two most deprived quintiles (ADI ≥60%) had a significantly higher risk of ischemic events and those in the most deprived quintile (ADI≥80%) had a significantly increased risk of bleeding events. ADI significantly improved the predictive performance of CHA2DS2-VASc but not HAS-BLED risk scores. CONCLUSION: ADI can predict increased risk for ischemic and bleeding events in the first year of warfarin therapy in patients with incident AF.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".