6152A novel model for prediction of ischemic stroke in patients without atrial fibrillation
Bibliographic record
Abstract
Introduction: Patients diagnosed with atrial fibrillation (AF) are candidates for oral anticoagulant treatment if their annual risk of stroke is above approximately 1% assessed by the CHA2DS2-VASc score. However, most patients suffering stroke, have no diagnosis of AF prior to their stroke. Purpose: To construct a risk prediction model for identification of patients at high risk of stroke and systemic embolism among patients without a diagnosis of AF, with no prior stroke, which may be useful for the decision making on primary thromboprophylaxis. Methods: Using national registries, we cross-linked data on patients undergoing coronary angiography to identify 72,381 patients without AF, prior stroke, or any anticoagulant treatment. The cohort was randomly divided into two groups; a training cohort (80%, n=57,680) and a validation cohort (20%, n=14,701). We used a composite endpoint of thromboembolic events including ischemic stroke, transient ischemic attack (TIA) and, systemic embolism. Considered covariates were first analysed by univariate analyses in the training cohort. All variables adding risk in stroke development (p<0.20) were afterwards included in a multivariate analysis. We performed interaction analyses before assigning points to the covariates in the final model.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".