Effects of the Driving Condition of a Pneumatic Ventricular Assist Device on the Cavitation Intensity of the Inlet and Outlet Mechanical Heart Valves
Bibliographic record
Abstract
Our group is currently developing a pneumatic ventricular assist device (PVAD), and in previous studies, we reported the mechanical heart valve (MHV) cavitation intensity at the inlet valve in the PVAD only. In this study, we investigated the effect of the running conditions on the cavitation intensity both for the inlet and outlet valve in the PVAD using an acoustic signal. A 23-mm Medtronic Hall valve with an opening angle of 70 degrees was mounted in the inlet and outlet port of the PVAD after removing the sewing ring. A mini pressure sensor with high frequency was mounted 15 mm downstream from the inlet valve and downstream from the outlet valve. The pressure signal was band-pass filtered between 35 and 500 kHz using a digital filter. The band-pass filtered root mean squared (RMS) pressure was used as an index of the cavitation intensity. The RMS pressure of the inlet valve was higher than that of the outlet valve. Even if the outlet valve has a lower RMS pressure than the inlet valve, cavitation occurs. In case of a full-filling and full-ejection condition, the RMS pressure of the inlet valve was higher than that of the partial-filling and partial-ejection condition. This means that a partial-filling and partial-ejection condition is best to prevent the hemolysis caused by the cavitation phenomenon and the damage to the valve surface in our PVAD system.
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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 | 0.002 |
| 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".