The Value of Left Internal Mammary Artery Flow Velocity in Predicting the Prognosis of Patients After Coronary Artery Bypass Grafting
Bibliographic record
Abstract
Background: The purpose of this study was to explore the value of the left internal mammary artery flow velocity (LIMAV) measured by ultrasound before coronary artery bypass grafting (CABG) in predicting the prognosis of patients after left internal mammary artery (LIMA) bypass grafting. Methods: One hundred and four patients who underwent CABG with LIMA as the bridge vessel in the cardiovascular surgery department of our hospital between May 2018 and June 2019 were selected. All patients underwent transthoracic Doppler ultrasonography to measure LIMAV preoperatively. Intraoperatively, mean graft flow (MGF) and pulsatility index (PI) of the LIMA bridge were measured using transit time flow measurement (TTFM). The primary endpoint event in this study was cardiac death within 18 months after surgery. Results: The Cox survival analysis showed that the MGF, the LIMAV and left ventricular ejection fraction (LVEF) were risk factors for death after CABG. The cut-offs of MGF, LIMAV and LVEF for the prediction of death after CABG were ≤ 14 mL/min (area under the curve (AUC): 0.830; sensitivity: 100%; specificity: 65.6%), ≤ 60 cm/s (AUC: 0.759; sensitivity: 65.5%; specificity: 85.3%), and ≤ 44% (AUC: 0.724; sensitivity: 50%; specificity: 88.5%), respectively. Compared with the use of MGF, MGF + LIMAV, combination of the MGF + LIMAV + LVEF (AUC: 0.929; sensitivity: 100%; specificity: 81.1%) resulted in a stronger predictive value (MGF vs. MGF + LIMAV + LVEF: P = 0.02). Conclusion: LIMAV measured by preoperative transthoracic ultrasound combined with intraoperative MGF and LVEF may have a greater value in predicting patients' risk of cardiac death after CABG.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".