Individual net clinical outcome with oral anticoagulation in atrial fibrillation using the ABC‐AF risk scores
Bibliographic record
Abstract
BACKGROUND: Decisions on stroke prevention strategies in patients with atrial fibrillation (AF) depend on the perceived risks of stroke and bleeding with different antithrombotic treatment strategies. The study objectives were to evaluate net clinical outcome with oral anticoagulation (OAC) for the individual patient with AF and to identify clinically relevant thresholds for OAC treatment. METHODS: Patients with AF receiving OAC treatment in the randomized ARISTOTLE and RE-LY trials, with available biomarkers for calculation of ABC-AF scores at baseline, were included (n = 23,121). Observed 1-year risk on OAC was compared with predicted 1-year risk if the same patients would not have received OAC using the ABC-AF scores calibrated for aspirin. Net clinical outcome was defined as the sum of stroke and major bleeding risks. RESULTS: The ratio between the 1-year incidence of major bleeding and stroke/systemic embolism events ranged from 1.4 to 10.6 according to different ABC-AF risk profiles. Net clinical outcome analyses showed that in patients with an ABC-AF-stroke risk >1% per year on OAC (>3% without OAC), treatment with OAC consistently provides larger net clinical benefit than no-OAC treatment. In patients with an ABC-AF-stroke risk <1.0% per year on OAC (<3% without OAC) an individualized balancing of risks regarding OAC or no-OAC treatment is needed. CONCLUSIONS: In patients with AF, the ABC-AF risk scores allow an individual and continuous estimate of the balance between benefits and risks with OAC treatment. This precision medicine tool therefore seems useful as decision support and visualizes the net clinical benefit or harm with OAC treatment (http://www.abc-score.com/abcaf/). CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT00412984 (ARISTOTLE) and NCT00262600 (RE-LY).
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".