Rapid Assessment of Relative Hemolysis Amidst Input Uncertainties in Laminar Flow
Bibliographic record
Abstract
Predicting absolute values of hemolysis using the power law model to guide medical device design is hampered by uncertainties stemming from four sources of model inputs: incoming/upstream velocity profiles, blood viscosity models, power law hemolysis coefficients, and obtaining accurate stress exposure times. Amidst all these uncertainties, enabling rapid assessments and predictions of relative hemolysis would still be valuable for evaluating device design prototypes. Towards achieving this objective, hemolysis data from the Eulerian modeling framework was first generated from computational fluid dynamics simulations encompassing five blood viscosity models, four sets of hemolysis power law coefficients, fully developed as well as developing velocity flow conditions, and a wide range of shear stresses, strain rates, and stress exposure times. Corresponding hemolysis predictions were also made in a Lagrangian framework via numerical integration of shear stress and residence time spatial variations under the assumption of fully developed Newtonian fluid flow. Absolute hemolysis predictions (from both frameworks) were proportional to each other and independent of the blood viscosity model. Further, relative hemolysis trends were not dependent on the hemolysis power law coefficients. However, accuracy in wall shear stresses in developing flow conditions is necessary for accurate relative hemolysis assessments.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".