Nonlinear Heart Rate Variability Analysis May Predict Atrial Fibrillation After Coronary Artery Bypass Grafting
Bibliographic record
Abstract
In Brief BACKGROUND: Heart rate variability might predict arrhythmias after coronary artery bypass grafting. METHODS: Off-line processing of 10-min electrocardiogram recordings of consecutive patients provided R–R intervals for time domain, frequency domain, Poincaré, and point correlation analyses and subsequent association with postoperative atrial fibrillation by stepwise multivariate logistic regression. RESULTS: Of 88 patients who met entry criteria, 13 developed atrial fibrillation. Peak point correlation dimension (odds ratio 3.985/unit, P = 0.0096) and age (odds ratio 1.144/yr, P = 0.0019) were independently associated with atrial fibrillation (c-statistic = 0.839). CONCLUSIONS: Further study should confirm the ability of peak point correlation dimension to predict atrial fibrillation after coronary artery surgery with cardiopulmonary bypass. IMPLICATIONS: Atrial fibrillation after coronary artery surgery prolongs hospital stay and complicates care. Predicting this outcome would allow targeted prophylaxis. Heart rate variability measures in this study indicate that peak point correlation dimension (PD2), a nonlinear measure, best associates with cardiac arrhythmias, warranting further investigation.
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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 | 0.004 |
| 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) | 0.001 | 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".