Preoperative poor coronary collateral circulation can predict the development of atrial fibrillation after coronary artery bypass graft surgery
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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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 | 0.000 | 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 | 0.001 |
| 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".