Time-varying differences in stroke recurrence risk between types of atrial fibrillation based on screening methods and timing of detection
Bibliographic record
Abstract
INTRODUCTION: Atrial fibrillation (AF) burden progresses with time. Among ischemic stroke (IS) patients, AF can be detected at different burden progression stages based on the timing and screening method. We hypothesized that AF detected after IS on 12-lead ECGs (ECG-AF) and via 14-day-Holter prolonged cardiac monitoring (AFDAS) are linked to lower IS recurrence risk than AF known before stroke occurrence (KAF) because of being at an earlier progression stage than KAF. Additionally, we posited that IS recurrence risk differences between AF types vary over time due to their differential progression stages. PATIENTS AND METHODS: Retrospective observational cohort study including IS/TIA patients with KAF, ECG-AF, and AFDAS [2018-2021]. Adjusted hazard ratios (aHR) were estimated using multivariable cause-specific Cox proportional-hazard models to compare IS recurrence between ECG-AF versus KAF and AFDAS versus KAF. Proportional hazards assumptions were tested to assess whether IS recurrence risk differences were time-varying. RESULTS: Of 758 AF patients (385 KAF, 236 ECG-AF, 137 AFDAS), 603 received anticoagulation and 59 experienced a recurrent IS after 1441 patient-years of follow-up. No IS recurrence risk differences were observed at the end of follow-up between ECG-AF and KAF (aHR 0.67, 95% CI 0.36-1.26), although ECG-AF showed lower risk only within the first year (aHR 0.15; 95% CI 0.04-0.56). AFDAS exhibited a lower IS recurrence risk than KAF (aHR 0.22, 95% CI 0.08-0.63), without time-varying differences. DISCUSSION: Differences in IS recurrence risk between ECG-AF and KAF varied over time. However, AFDAS showed a consistently lower IS risk than KAF throughout the entire study period.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".