Predictors for atrial fibrillation detection after cryptogenic stroke
Bibliographic record
Abstract
OBJECTIVE: We assessed predictors of atrial fibrillation (AF) in cryptogenic stroke (CS) or transient ischemic attack (TIA) patients who received an insertable cardiac monitor (ICM). METHODS: We studied patients with CS/TIA who were randomized to ICM within the CRYSTAL AF study. We assessed whether age, sex, race, body mass index, type and severity of index ischemic event, CHADS2 score, PR interval, and presence of diabetes, hypertension, congestive heart failure, or patent foramen ovale and premature atrial contractions predicted AF development within the initial 12 and 36 months of follow-up using Cox proportional hazards models. RESULTS: Among 221 patients randomized to ICM (age 61.6 ± 11.4 years, 64% male), AF episodes were detected in 29 patients within 12 months and 42 patients at 36 months. Significant univariate predictors of AF at 12 months included age (hazard ratio [HR] per decade 2.0 [95% confidence interval 1.4-2.8], p = 0.002), CHADS2 score (HR 1.9 per one point [1.3-2.8], p = 0.008), PR interval (HR 1.3 per 10 milliseconds [1.2-1.4], p < 0.0001), premature atrial contractions (HR 3.9 for >123 vs 0 [1.3-12.0], p = 0.009 across quartiles), and diabetes (HR 2.3 [1.0-5.2], p < 0.05). In multivariate analysis, age (HR per decade 1.9 [1.3-2.8], p = 0.0009) and PR interval (HR 1.3 [1.2-1.4], p < 0.0001) remained significant and together yielded an area under the receiver operating characteristic curve of 0.78 (0.70-0.85). The same predictors were found at 36 months. CONCLUSION: Increasing age and a prolonged PR interval at enrollment were independently associated with an increased AF incidence in CS patients. However, they offered only moderate predictive ability in determining which CS patients had AF detected by the ICM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".