Prevalence and Predictors of Paroxysmal Atrial Fibrillation on Holter Monitor in Patients With Stroke or Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Our aims were to quantify the yield of Holter monitor for detection of paroxysmal atrial fibrillation (PAF) in patients with stroke and TIA, and to determine potential predictors of PAF to allow more focused testing. METHODS: We reviewed records of 1128 consecutive patients attending a university stroke clinic from September 2005 to September 2006 and identified 426 patients with definite TIA or stroke. We abstracted clinical, cardiac imaging, and neuroimaging data. Logistic regression analysis was performed to determine independent predictors of PAF on Holter monitor. RESULTS: Overall, 413 of 426 patients (65 ± 15 years; male, 49.8%) with a definite TIA (53%) or stroke (47%) underwent Holter monitoring for a mean of 22.6 hours. PAF occurred in 39 patients (9.2%) all older than age 55 years. PAF lasting > 30 seconds was evident in 11 patients (2.5%). The other 28 patients had PAF < 30 seconds (6.5%). In multivariate analyses, number of acute (odds ratio [OR], 1.7 for each 1 lesion increase; 95% confidence interval [CI], 1.2-2.6; P = 0.0047) and chronic (OR, 1.6 for each 1 lesion increase; 95% CI, 1.2-2.3; P = 0.0001) infarcts on brain CT, number of chronic infarcts on MRI (OR, 3.0 for each 1 lesion increase; 95% CI, 1.7-5.1; P < 0.0001), and any acute cortical infarct on imaging (OR, 5.8; 95% CI, 1.9-17.8; P = 0.0023) were associated with PAF. CONCLUSIONS: PAF is present in 9.2% of patients with definite stroke or TIA. Age older than 55 years and presence of acute or chronic brain infarcts on neuroimaging are strongly associated with PAF.
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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".