How pulmonary valve regurgitation after tetralogy of fallot repair changes the flow dynamics in the right ventricle: An <i>in vitro</i> study
Bibliographic record
Abstract
Tetralogy of Fallot is the most common cyanotic congenital disease, affecting 10% of children with congenital heart disease. The surgical management of patients with Tetralogy of Fallot leads, however, to significant detrimental effects on the right ventricle including pulmonary valve regurgitation. This experiment aimed to simulate different cases of pulmonary valve regurgitation with varying degrees of severity in order to observe the changes in flow structures present in the right ventricle. Planar time-resolved particle image velocimetry measurements have been performed on a custom-made double activation simulator reproducing flow conditions in a model of a right ventricle. Changes in flow characteristics in the right ventricle have been evaluated in terms of velocity fields and profiles, tricuspid inflow jet orientation and viscous energy dissipation. Our results show that pulmonary valve regurgitation significantly alters the flow in the right ventricle mostly by impairing the diastolic inflow through the tricuspid valve and by increasing viscous energy loss. This fundamental work should allow for a better understanding of such changes in the RV flow dynamics. It may also help in developing new strategies allowing for a better follow-up of patients with repaired TOF and for decision-making in terms of pulmonary valve replacement.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".