Wall Shear Stress Differences Between Arterial and Venous Coronary Artery Bypass Grafts One Month After Surgery
Bibliographic record
Abstract
Abstract Although coronary artery bypass graft (CABG) surgery is a well-established intervention, graft failure can occur, and the underlying mechanisms remain incompletely understood. The purpose of this prospective study is to utilize computational fluid dynamics (CFD) to investigate how graft hemodynamics one month post surgery may vary among graft types, which have different long-term patency rates. Twenty-four grafts from 10 participants (64.6 ± 8.5 years, 9 men) were scanned with coronary CT angiography and 4D flow MRI one month after CABG surgery. Grafts included 10 left internal mammary arteries (LIMA), 3 radial arteries (RA), and 11 saphenous vein grafts (SVG). Image-guided CFD was used to quantify blood flow rate and wall area exposed to abnormal wall shear stress (WSS). Arterial grafts had a lower abnormal WSS area than venous grafts (17.9% vs. 70.1%; p = 0.001), and a similar trend was observed for LIMA vs. SVG (13.8% vs. 70.1%; p = 0.001). Abnormal WSS area correlated positively to lumen diameter (p < 0.001) and negatively to flow rate (p = 0.001). This CFD study is the first of its kind to prospectively reveal differences in abnormal WSS area 1 month post surgery among CABG types, suggesting that WSS may influence the differential long-term graft failure rates observed among these groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".