Comparison of Clinical Risk Stratification for Predicting Stroke and Thromboembolism in Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Several accepted algorithms exist to characterize the risk of thromboembolism in atrial fibrillation. We performed a comparative analysis to assess the predictive value of 9 such schemes. METHODS: In a longitudinal community-based cohort study from Olmsted County, Minnesota, 2720 residents with atrial fibrillation were followed up for 4.4±3.6 years±SD from 1990 to 2004. Risk factors were identified using a diagnostic index integrated with the electronic medical record. Thromboembolism and cardiovascular event data were collected and analyzed. RESULTS: We identified 350 validated thromboembolic events in our cohort. Multivariable analysis identified age >75 years (odds ratio, 2.08; P<0.0001), female sex (odds ratio, 1.45; P=0.0015), history of hypertension (odds ratio, 3.07; P<0.0001), diabetes mellitus (odds ratio, 1.58; P=0.0003), and history of heart failure (odds ratio, 1.50; P=0.0102) as significant predictors of clinical thromboembolism. The Stroke Prevention in Atrial Fibrillation (SPAF; hazard ratio, 2.75; c=0.659), CHADS2-revised (hazard ratio, 3.48; c=0.654), and CHADS2-classical (hazard ratio, 2.90; c=0.653) risk schemes were most accurate in risk stratification. The low-risk cohort within the CHA2DS2-VASc scheme had the lowest event rate among all low-risk cohorts (0.11 per 100 person-years). CONCLUSIONS: A direct comparison of 9 risk schemes reveals no profound differences in risk stratification accuracy for high-risk patients. Accurate prediction of low-risk patients is perhaps more valuable in determining those unlikely to benefit from oral anticoagulation therapy. Among our cohort, CHA2DS2-VASc performed best in this purpose.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".