Pulmonary regurgitation after tetralogy of fallot repair: A diagnostic and therapeutic challenge
Bibliographic record
Abstract
BACKGROUND: Pulmonary regurgitation is the key hemodynamically significant lesion in repaired tetralogy of Fallot contributing to progressive right ventricular (RV) dilatation and biventricular dysfunction. The timing for pulmonary valve replacement remains a controversial topic, and the decision to intervene depends on assessment of RV size and RV function. OBJECTIVES: This review aims to discuss the echocardiographic techniques that can be used to assess patients with pulmonary regurgitation after the repair of tetralogy of Fallot defect. While cardiac magnetic resonance (CMR) imaging is the clinical reference method, there is an important role of echocardiography in identifying patients with significant pulmonary regurgitation and assessing the RV size and function. The different echocardiographic techniques that can be used in this context are discussed. Newer techniques for assessing RV size and function include three-dimensional (3D) echocardiography, tissue Doppler and strain imaging. 3D RV volumetric reconstruction based on two-dimensional imaging is a promising new technique that could potentially replace CMR for RV volumetric assessment. CONCLUSIONS: Developments in echocardiographic techniques provide new insights into the impact of pulmonary regurgitation on RV structure and function. Echocardiography and CMR are complementary modalities and further research is required to define the optimal use of both techniques for this indication.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".