Abstract P617: Ischemic Strokes in Patients With Atrial Fibrillation: The Neuro-AFib Study
Bibliographic record
Abstract
Background: An estimated 150,000 atrial fibrillation (AF) patients suffer an ischemic stroke (IS) annually in the US. Understanding the frequency/causes of underuse and failures of current FDA-approved preventive methods in patients with known AF may reduce stroke risk and related death/disability. Methods: The Neuro-AFib study is a multicenter effort geared toward elucidating the causes and consequences of IS and hemorrhagic stroke (HS) in a contemporary AF cohort. The retrospective phase of the study is underway, aiming to obtain detailed clinical, laboratory and multimodal neuro- and cardiac imaging data from ~9,000 AF patients admitted to 30 US academic stroke centers with an IS or HS between 1/2018-12/2019. Clinical data of IS admissions from 12 sites will be discussed. Disability is defined as a modified Rankin Score (mRS) 3-5, outcomes are from the time of hospital discharge. Results: A total of 3944 AF patients presented with an IS, mean age was 76.8 + 12, and 50.2% were female. AF was diagnosed prior to IS in 78% of patients. Data on prestroke antithrombotic usage, embolic risk scores, clinical stroke severity and outcomes are presented in the FIGURE. Conclusions: Preliminary results from the Neuro-AFib study show high rates of underuse of approved stroke prevention measures (54%) and anticoagulant failures (46%) that result into IS even in known AF patients. Relatively high rates of pre-stroke AF detection failures were also noted (22%). Death/disability rates were high in all of these AF-related IS patients ( > 69%). Detailed data collection focused on imaging and lab markers of stroke risk from this contemporary cohort will be ready to be presented during ISC 2021.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".