Experimental Investigation of VSS in Virtual Red Blood Cells andPlatelets in the Flow of a PVAD Using Pseudo-Tracking Analysis
Bibliographic record
Abstract
Ventricular assist devices (VAD) improve patient's survival rates at the heart transplant waiting list.Notwithstanding, VADs are not perfect replacements for a failing heart having their design and operational issues resulting in hemolysis and thrombogenesis.Both blood degradation processes are associated with viscous shear stress (VSS).Therefore, one way to experimental evaluate VSS values along residence time that red blood cells and platelets are subject to is pseudo-tracking.In pseudo-tracking method a postprocessing is done in particle image velocimetry (PIV) data, so particles can be generated inside the velocity field and their pathlines will provide VSS values.For better accuracy in this Lagrangian approach, temporal resolution is a must.Therefore, time-resolved PIV (TR-PIV) technique for the VAD is a solution.In this work, the pneumatic pediatric assist device (PVAD) developed by Instituto do Coração (InCor) was measured using a TR-PIV at 70 beats per minute (bpm), and a pseudo-tracking analysis developed at MATLAB was done with two reconstructed particles.The first reconstructed particle was created near the inlet valve and the second near the outlet valve.Both reconstructed particles were generated at the beginning of PVAD's diastole and their instantaneous and integrated VSS with resident time values were compared to literature thresholds for erythrocytes and thrombocytes.
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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 | 0.001 | 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 | 0.000 | 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".