Preoperative poor coronary collateral circulation can predict the development of atrial fibrillation after coronary artery bypass graft surgery
Why this work is in the frame
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Bibliographic record
Abstract
AIM: Coronary collateral circulation (CCC) helps to protect and preserve myocardium from episodes of ischemia, and reduce angina symptoms, arrhythmia, and cardiovascular events. Atrial fibrillation (AF) is the most frequent form of arrhythmia after coronary artery bypass graft (CABG) surgery. The aim of this study was to investigate the association between CCC and the development of AF in patients undergoing CABG surgery. METHODS: A total of 165 patients (mean age 63±10 years, 74% men, 26% women) who were undergoing CABG surgery at our department were enrolled into this study. Patients were categorized into two groups according to preoperative CCC using the Rentrop method. RESULTS: Of the patients, 79 had poor CCC and 89 had good CCC. The AF incidence rate in the poor collateral group was significantly higher than that in the good collateral group [37 (49%) vs. 12 (14%), P<0.001]. In univariate analysis, age, left atrium size, and poor CCC grade were associated with AF after CABG surgery. Multivariate analysis showed that only poor CCC grade (odds ratio: 11.500; 95% confidence interval 3.977-33.253, P<0.001) was an independent predictor of the development of AF after adjustment of other potential confounders in patients undergoing CABG surgery. CONCLUSION: The present study showed that preoperative poor CCC is a powerful predictor of the development of AF after CABG surgery.
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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) | 0.001 | 0.001 |
| 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 it