Physical replication and validation of mathematical mitral valve models
Bibliographic record
Abstract
Accurate models of the mitral valve are highly valuable for studying the physiology of the heart and its various pathologies, as well as creating physical replicas for cardiac surgery training. Currently, heart simulator technologies are used which rely on patient-specific data to create valve replicas. Alternatively, mathematical models of the mitral valve have been developed for computational applications. However, there are no studies that mathematically model both the mitral valve’s leaflets and its saddle-shaped annulus in a single design together in current literature. This results in anatomic inaccuracies in current models, as either only the leaflets or the saddle-shaped annulus are realistically modelled. Mathematical models to date have not been replicated as dynamic, physical valves and validated in a heart simulator system. We propose a new parametric representation of the mitral valve based on a combination of valve models from prior literature, combining both accurate leaflet shape, and annular geometry. A physical silicone replica of the model is created and validated in a pulse duplicator. Using a transesophageal echocardiography probe with color Doppler imaging, we demonstrate that our combined model replicates healthy valve behaviour, showing no regurgitation at realistic pressure gradients across the valve.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".