Ischemic Stroke Risk in Patients With Atrial Fibrillation and CHA <sub>2</sub> DS <sub>2</sub> -VASc Score of 1
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The CHA2DS2-VASc score aims to improve risk stratification of ischemic stroke among patients with atrial fibrillation to identify those who can safely forego oral anticoagulation. Oral anticoagulation treatment guidelines remain uncertain for CHA2DS2-VASc score of 1. We conducted a systematic review and meta-analysis of the risk of ischemic stroke for patients with atrial fibrillation and CHA2DS2-VASc score of 0, 1, or 2 not treated with oral anticoagulation. METHODS: We searched MEDLINE, Embase, PubMed, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from the start of the database up until April 15, 2015. We included studies that stratified the risk of ischemic stroke by CHA2DS2-VASc score for patients with nonvalvular atrial fibrillation. We estimated the summary annual rate of ischemic stroke using random effects meta-analyses and compared the estimated stroke rates with published net-benefit thresholds for initiating anticoagulants. RESULTS: 1162 abstracts were retrieved, of which 10 met all inclusion criteria for the study. There was substantial heterogeneity among studies. The summary estimate for the annual risk of ischemic stroke was 1.61% (95% confidence interval 0%-3.23%) for CHA2DS2-VASc score of 1, meeting the theoretical threshold for using novel oral anticoagulants (0.9%), but below the threshold for warfarin (1.7%). The summary incident risk of ischemic stroke was 0.68% (95% confidence interval 0.12%-1.23%) for CHA2DS2-VASc score of 0 and 2.49% (95% confidence interval 1.16%-3.83%) for CHA2DS2-VASc score of 2. CONCLUSIONS: Our meta-analysis of ischemic stroke risk in atrial fibrillation patients suggests that those with CHA2DS2-VASc score of 1 may be considered for a novel oral anticoagulant, but because of high heterogeneity, the decision should be based on individual patient characteristics.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".