A computational fluid dynamics simulation study of coronary blood flow affected by graft placement
Bibliographic record
Abstract
OBJECTIVES: To determine the effect of graft placement and orientation on flow rates through a partially obstructed coronary artery. METHODS: A numerical, parametric study of blood flow in the human coronary artery was conducted using computational fluid dynamics simulation. A cylindrical approximation of the coronary artery with varying degrees of stenosis, with and without a bypass graft, was modelled to determine trends in volumetric flow rates. Steady and transient simulations were conducted for geometric variations of percentage of blockage, length and shape of stenosis, graft position relative to the coronary blockage and graft orientation. Accurate simulations were performed using a non-Newtonian fluid model and pressure-driven viscous flow. RESULTS: Simulations demonstrate, as expected, that total outlet flow rates of grafted arteries are consistently improved for upstream stenosis varying between 0 and 90% blockage. Grafts angled towards the artery provided increased total outflow. However, flow rates in the coronary artery upstream of the graft are substantially reduced in comparison with the non-grafted configuration due to competing flows. For some configurations (reduced blockage, graft placed close to long grafts), flow rates in the graft are below that of the flow rate through the stenosis. In general, a graft angled more towards the artery increased flow rates upstream of the graft. CONCLUSIONS: Placement and orientation of a graft may adversely affect upstream flow, with the degree of effect dependent on geometric factors of downstream position and graft angle.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".