The Ross Procedure in Children with Congenital Heart Disease
Bibliographic record
Abstract
Aortic valve disease accounts for approximately 5% of all congenital heart defects in children. Choosing the optimal valve replacement in this population is challenging, as it must ensure durability, accommodate growth, and minimize the need for long-term anticoagulation. Biological valves do not require anticoagulation but lack durability and growth potential, leading to frequent reoperations. Mechanical valves offer longevity but necessitate lifelong anticoagulation and do not grow with the child. Among the available surgical options, the Ross procedure has emerged as a preferred approach due to its favorable hemodynamic performance, growth potential, and freedom from anticoagulation. First described in 1967, this technique involves replacing the diseased aortic valve with a pulmonary autograft and reconstructing the right ventricular outflow tract using a human or non-human valve substitute. Despite its advantages, the procedure is technically demanding, has a considerable learning curve, and transforms a single-valve pathology into a bivalvular condition. This narrative review provides an updated perspective on the Ross procedure in children, focusing on long-term survival, reoperation rates, and the role of percutaneous valve replacement in delaying surgical reintervention. By synthesizing the latest evidence, we aim to clarify the current standing of the Ross procedure as a durable and effective solution for pediatric aortic valve disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".