Sensitivity Analysis of a Hemodynamic-based Model for Thrombus Formation and Growth
Bibliographic record
Abstract
Aortic dissection is a severe cardiovascular disease caused by the occurrence of a tear in the aortic wall.As a result, the blood penetrates the wall and makes a new blood channel called false lumen.The hemodynamic conditions in the false lumen may contribute to the formation of thrombi, which influence the patient diagnosis and outcomes.In this study, the focus is on a hemodynamic-based model of thrombus formation.Since the model construction presents sources of uncertainties in the model parameter, a variance-based sensitivity analysis is performed.Thrombus formation at a backwards-facing step is considered as a benchmark for the numerical simulations and sensitivity analysis.This geometry is capable of representing the main contributions of the model in thrombus formation.The study aims not only at getting a better insight into the model's structure but also at preparing model simplifications with the aim of future patient-specific simulations.A polynomial chaos expansion is employed as a surrogate model, from which the derivation of quantitative sensitivity indices is enhanced.In this study, nine model parameters are selected, whose actual values are not well known.The model responses taken into account are the maximum volume fraction of thrombosis, its time development, and the thrombus growth rate.The results show that the model lends itself to model reduction since some of the model parameters show little to no influence on the model's outputs.A threshold value related to the concentration of bounded platelets is identified as the key input parameter dominating the model predictions in the current geometry.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".