Aortic valve repair for insufficiency in older children offers unpredictable durability that may not be advantageous over a primary Ross operation
Bibliographic record
Abstract
OBJECTIVES: To evaluate the durability of aortic valve (AoV) repair relative to other strategies for children with significant aortic insufficiency (AI). METHODS: From 2001 to 2012, 90 children with greater than or equal to moderate AI underwent surgery. Resulting procedures were classified according to final operative outcome: AoV repair (repair; n = 46, 51%), Ross procedure (Ross; n = 21, 23%) or replacement with mechanical or tissue prosthesis [aortic valve replacement (AVR); n = 23, 26%]. Repeated measures (n = 1081 echocardiograms) mixed-model analysis and parametric multiphase risk-adjusted hazard analysis were used to evaluate haemodynamic parameters and durability of operations. RESULTS: Mean age at operation was similar for repair and Ross groups, but slightly higher for the AVR group (10.6, 11 and 13.2, respectively; P = 0.04). Baseline annular dimensions were similar among groups. Of 46 repairs, 85% involved pericardial leaflet extensions (commonly with leaflet shaving and/or commisuroplasty). The remaining repairs were commissuroplasties. On multivariable analysis, repair was associated with increased early (∼1-2 years) AI and increased outflow tract peak pressure gradients relative to Ross and AVR procedures. On univariate analysis, repairs tended to have a larger annulus size compared with Ross or AVR; however, this was not significant on multivariable analysis. There were 25 reinterventions (surgical reoperation = 16; transcatheter intervention = 9) for 22 children. Freedom from surgical reoperation was 64, 100 and 51% at 6 years for repairs, Ross and AVR, respectively (P = 0.05); however, three of five reoperations after AVR were for failed bioprosthetic devices. The freedom from reintervention was not significantly influenced by the type of AoV operation (P = 0.43). CONCLUSIONS: Durability of aortic valve repair for children is limited by recurrence of AI and/or stenosis, often within the first few years. After repair, reoperation should be anticipated within ∼7 years.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| 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".