ADVANTAGES AND DISADVANTAGES OF HEART VALVE PROSTHESES WITH FLEXIBLE SUPPORTING FRAME COMPARED WITH THE CLASSIC STENTED PROSTHESES: EVALUATION OF THE HYDRODYNAMIC INDICATORS OF THE “UNILINE” AND “TIARA” BIOPROSTHESES
Bibliographic record
Abstract
Highlights The «TiAra» bioprosthetic heart valves have better hemodynamic characteristics, such as higher effective orifice area and a lower mean pressure gradient. The «UniLine» bioprosthetic heart valve demonstrated better closing dynamic, expressed in a smaller regurgitation volume. Aim. To assess hydrodynamic characteristics of the «TiAra» bioprosthetic heart valve with flexible supporting frame compared with the classic stented «UniLine» bioprosthetic aortic valve. Methods. Using the Vivitro Pulse Duplicator (Vivitro Labs, Canada), we simulated the function of the heart via generating pulsatile flow to analyze bioprosthetic heart valves. To comprehensively assess the bioprosthesis function, three valves of each standard size (21, 23, 25 mm) were submitted to hydrodynamic testing, thus making a sample of nine bioprostheses of each model. The article provides the analysis of the effective orifice area, mean pressure gradient, regurgitation volume, and assessment of the statistical sensitivity of the parameters between groups at p = 0.05. Results. The assessment revealed that the «TiAra» bioprosthesis has bigger effective orifice area (p = 0.006) and lower mean pressure gradient (p = 0.02): 1.6–2.2 cm2 and 3.6–6.3 mmHg versus 1.08–1.73 cm2 and 4.8–12.1 mmHg, respectively. The regurgitation volume, however, was lower in the «UniLine» bioprostheses 0.8–4.1 mL/cycle versus 6.2–9.0 mL/cycle (p = 0.0004). Conclusion. Despite the fact that both studied models showed good hydrodynamic performance, the prosthesis with the flexible supporting frame («TiAra») showed better results regarding its effectiveness in vitro via presenting with bigger effective orifice area and lower mean pressure gradient. At the same time, the «UniLine» stented bioprosthesis had lower regurgitation volume, i.e. better closing dynamics.
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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 | 0.001 |
| 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".