Influence of vortical structures on fibrin clot formation in cerebral aneurysms: A two-dimensional computational study
Bibliographic record
Abstract
Thrombosis is an important contributor to cerebral aneurysm growth and progression. A number of sophisticated multiscale and multiphase in silico models have been developed with a view towards interventional planning. Many of these models are able to account for clotting outcomes, but do not provide detailed insight into the role of flow during clot development. In this study, we present idealised, two-dimensional in silico cerebral fibrin clot model based on computational fluid dynamics (CFD), biochemical modelling and variable porosity, permeability, and diffusivity. The model captures fibrin clot growth in cerebral aneurysms over a period at least 1000 s in five different geometries. The fibrin clot growth results were compared to an experiment presented in literature. The biochemistry was found to be more sensitive to mesh size compared to the haemodynamics, while larger timesteps overpredicted clot size in pulsatile flow. When variable diffusivity was used, the predicted clot size was 25.4% lesser than that with constant diffusivity. The predicted clot size in pulsatile flow was 14.6% greater than in plug flow. Different vortex modes were observed in plug and pulsatile flow; the latter presented smaller intermediate modes where the main vortex was smaller and less likely to disrupt the growing fibrin clot. Furthermore, smaller vortex modes were seen to support fibrin clot propagation across geometries. The model clearly demonstrates how the growing fibrin clot alters vortical structures within the aneurysm sac and how this changing flow, in turn, shapes the growing fibrin clot.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".