Advanced Imaging Technologies for Assessing Tetralogy of Fallot: Insights Into Flow Dynamics
Bibliographic record
Abstract
Tetralogy of Fallot is the most common cyanotic congenital heart defect requiring surgical repair. Although surgical interventions have significantly reduced mortality, postrepair complications, such as pulmonary valve regurgitation and stenosis, may lead to adverse outcomes, including right ventricular dysfunction and increased risks of morbidity and mortality. This review explores the potential of advanced imaging technologies, including 4-dimensional-flow magnetic resonance imaging and high-frame-rate echocardiography, in providing valuable insights into blood flow dynamics and energy parameters. Quantitative measures, such as energy loss and vorticity, along with qualitative flow analysis, can provide additional insights into adverse haemodynamics at a potentially earlier and more reversible stage. Furthermore, personalized patient-specific information from these imaging modalities aids in guiding treatment decisions and monitoring postoperative interventions effectively. By characterizing flow patterns, these advanced imaging techniques hold great promise in improving the assessment and management of tetralogy of Fallot, providing tailored insights. However, further research and longitudinal studies are required to fully establish their clinical utility and potential impact on patient care.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".