Abstract 100: Influence of Race on Presentation, Treatment, and Outcome Among Patients With Atrial Fibrillation-related Ischemic Stroke
Bibliographic record
Abstract
Background: Blacks have been underrepresented (<2% of 71,683) in recent atrial fibrillation (AF) trials of novel anticoagulants vs. warfarin. Blacks with AF have also been underrepresented in stroke cohort studies from which the stroke risk prediction rules are derived. Objective: We examined whether there exist racial differences in presentation, treatment, and outcome among patients with AF-related ischemic stroke (IS). Methods: Consecutive IS were identified from 2006-2010 at 3 U.S. sites. All events were evaluated by CT or MRI, and assigned a discharge modified Rankin score (mRS). AF was confirmed by ECG. Baseline medications and clinical characteristics were abstracted from the medical record. Race was determined by self-report. Findings: We identified 1,030 AF-related IS; 96% (n=985) had race reported as White (n=764, 74%) or Black (n=221, 21%). Compared to Whites, Blacks were younger, had a higher burden of risk factors, had higher prevalence of paroxysmal or new onset AF, and more often presented outside the t-PA window. Among patients with known AF, 40% Whites and 39% Blacks were taking warfarin on admission. INR on admission was not different by race (mean 1.4, SD =0.7; p=0.64). These strokes resulted in severe neurological deficit (mRS >3) in a majority of Blacks and Whites (70% vs. 64%; p=0.09). Conclusion: Blacks with AF who suffer IS are younger, have a higher burden of risk factors, and more often present with paroxysmal or new onset AF. IS in the setting of AF was associated with significant mortality and morbidity in both Blacks and Whites.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".