Outcomes and Associated Risk Factors for Aortic Valve Replacement in 160 Children
Bibliographic record
Abstract
BACKGROUND: We sought to define patient characteristics, outcomes, and associated risk factors after aortic valve replacement (AVR) in children. METHODS AND RESULTS: Clinical records from children undergoing AVR from 1974 to 2004 at our institution were reviewed. Competing-risks methodology determined the time-related prevalence of 3 mutually exclusive end states: death, repeated replacement, and survival without subsequent AVR and their associated risk factors. Longitudinal echocardiographic data were analyzed by mixed linear-regression models. Children (n=160) underwent 198 AVRs, with 33 having >1. Competing-risks analysis predicted that 10 years from the initial AVR, 19% had died without subsequent AVR, 34% underwent a second AVR, and 47% remained alive without replacement. Risk factors for death without a second AVR included lower weight (P<0.001) and younger age at AVR (P=0.04), performance of aortic arch reconstruction together with AVR (P=0.03), and nonautograft use (P=0.03). Risk factors for a second AVR included earlier operation year (P=0.04) and implantation of a bioprosthetic or homograft valve (P=0.004). Analysis of serial echocardiographic measurements showed that pulmonary autograft use was associated with slower progression of peak aortic gradient (P=0.002), smaller left ventricular dimension (P=0.04), and decreased prevalence of aortic regurgitation (P=0.04). CONCLUSIONS: Mortality and repeated valve replacement are common after initial AVR in children, especially in younger patients and those with bioprosthetic or homograft valves. Pulmonary autograft use is associated with decreased mortality, slower gradient progression, and smaller left ventricular dimension.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".