Association of brain infarcts and cardiac autonomic dysfunction using entropy functions in patients with atrial fibrillation
Bibliographic record
Abstract
Abstract Background Cardiac autonomic dysfunction after brain infarcts is a strong predictor of adverse outcome. Purpose The aim of this study was to investigate the association of large cortical or non-cortical brain infarcts (LNCCI) with multiscale (MSE) and sample entropy (SE), which characterize the complexity of RR interval time series in atrial fibrillation patients. Methods In this cross-sectional analysis, we enrolled 1085 patients (mean age 72±9 years, 29% female) from the Swiss-AF cohort study with baseline brain MRI and without a paced rhythm on a 5-minute resting 12-lead ECG (61% in SR and 39% in AF). We constructed linear regression models to analyze the association between presence of LNCCI and entropy markers, adjusted for age, gender, intake of betablockers, diabetes and heart failure. Results On brain MRI, LNCCI were present in 220 patients (20.3%). 53% of these brain infarcts were clinically silent. Patients with LNCCI had a higher MSE (median 11.4 (IQR 6.5-13.3) vs. 8.5 (IQR 5.2-12.6), p<0.001) and SE (median 2.6 (IQR 1.0-3.4) vs. 1.6 (IQR 0.8-3.2), p<0.001) compared to patients without LNCCI. After adjustment for possible confounders, the presence of LNCCI was independently associated with a higher MSE (β=0.77; 95% CI 0.19-1.35, p=0.010). This association persisted when patients with a history of stroke/TIA were excluded. Conclusions Brain infarcts, irrespective if clinically overt or silent, are associated with higher entropy markers in AF patients, which indicates cardiac autonomic disturbances.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".