New laboratory technique measures projected dynamic area of prosthetic heart valves.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: Fluid dynamic forces, valve design factors and gravity interactively determine the complex motion of prosthetic heart valve occluders. Although motion has been investigated, in vitro, using high-speed image recording, the technique has significant cost and limitations on resolution. METHODS: The kinematics of mechanical and biological valve occluders in the aortic and mitral positions were assessed by measuring projected dynamic valve area (PDVA). Valves were tested in a pulse duplicator system simulating normal cardiac conditions. To quantify PDVA, light passage through back-illuminated valves was measured by a calibrated photosensor with high-frequency response up to 150 kHz. Ten consecutive cycles were sampled using a PC data acquisition system. The system was calibrated under static conditions using reference areas. RESULTS: Several characteristics can be obtained from PDVA measurement including: maximum and minimum PDVA; rate of change of valve opening and closing PDVA; occluder rebound; and oscillatory open occluder behavior. Biological valves open more rapidly, close more gently, and exhibit no occluder rebound. They are also unaffected by gravity, and vary little in behavior from cycle to cycle compared with mechanical valves. CONCLUSION: A new method for measuring PDVA has been developed. Distinct differences in performance between valves were identified. It is hypothesized that, aside from patient factors and differences in materials, mechanical valves that mimic the PDVA behavior of biological valves, will lead to reduction of thrombogenicity, cavitation and high-intensity transient signals (HITS), and also reduce sound level and regurgitation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.004 | 0.002 |
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".