A Clinical Prediction Rule to Identify Patients With Atrial Fibrillation and a Low Risk for Stroke While Taking Aspirin
Bibliographic record
Abstract
BACKGROUND: We sought to derive and internally validate a simple and easily applied clinical prediction rule to identify patients with nonvalvular atrial fibrillation (AF) whose stroke risk while taking aspirin is, irrespective of age, low enough that oral anticoagulation therapy is unnecessary. METHODS: We included 2501 patients with AF treated with aspirin during participation in 6 clinical trials. Patients were randomly divided into derivation and validation sets. Recursive partitioning was used to identify patients in the derivation set whose risk for stroke (ischemic or hemorrhagic) or transient ischemic attack was comparable to that observed in an age- and sex-matched cohort from the Framingham Heart Study. The derived prediction rules were tested on the validation set. RESULTS: Overall, 166 patients (6.6%) had an event during 4688.6 person-years (PYs) of observation for an incident rate of 3.5 events per 100 PYs. Patients in the derivation set classified as low risk (no previous stroke or transient ischemic attack, no treated hypertension or systolic blood pressure equal to or exceeding 140 mm Hg, no symptomatic coronary artery disease, and no diabetes) experienced 1.0 events per 100 PYs, compared with an age- and sex-matched rate of 1.2 events per 100 PYs. In the validation set, low-risk patients experienced 1.1 events per 100 PYs (expected rate of 1.2 events per 100 PYs). Low-risk patients made up 24% of the cohort and 16% of patients older than 75 years. Low-risk patients who were randomized to therapeutic oral anticoagulation therapy experienced 1.5 events per 100 PYs. CONCLUSION: Irrespective of age, patients with AF and none of these 4 clinical features and who take aspirin have stroke rates comparable to those of age-matched community cohorts and would not benefit substantially from anticoagulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".