Uncovering Atrial Fibrillation Beyond Short-Term Monitoring in Cryptogenic Stroke Patients
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) can be a cause of previously diagnosed cryptogenic stroke. However, AF can be paroxysmal and asymptomatic, thereby making detection with routine ECG methods difficult. Oral anticoagulation is highly effective in reducing recurrent stroke in patients with AF, but its initiation is dependent on the detection of AF. Cryptogenic Stroke and Underlying Atrial Fibrillation (CRYSTAL AF) is the first randomized study to report the detection of AF in cryptogenic stroke patients using continuous long-term monitoring via insertable cardiac monitors (ICM). METHODS AND RESULTS: Patients with prior cryptogenic stroke were randomized to control (n=220) or ICM (n=221) and followed for ≤36 months. Cumulative AF detection rates in the ICM arm increased progressively during this period (3.7%, 8.9%, 12.4%, and 30.0% at 1, 6, 12, and 36 months, respectively), but remained low in the control arm (3.0% at 36 months). This resulted in oral anticoagulation prescription in 94.7% of ICM patients with AF detected at 6 months, 96.6% at 12 months, and 90.5% at 36 months. Among ICM patients with AF detected, the median time to AF detection was 8.4 months, 81.0% of first AF episodes were asymptomatic, and 94.9% had at least 1 day with >6 minutes of AF. CONCLUSIONS: Three-year monitoring by ICM in cryptogenic stroke patients demonstrated a significantly higher AF detection rate compared with routine care. Given the frequency of asymptomatic first episodes and the long median time to detection, these findings highlight the limitations of using traditional AF detection methods. The majority of patients with AF were prescribed oral anticoagulation therapy. CLINICAL TRIAL REGISTRATION: Clinicaltrials.gov; Unique identifier: NCT00924638.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".