Abstract 17386: The Survival Impact of Genetic and Chromosomal Aberrations, Non-Cardiac Congenital Defects and Acquired Baseline Morbidity on Neonates with Congenital Heart Disease. How Does the “Perfect” Child Fare?
Bibliographic record
Abstract
Background Genetic aberrations, congenital non-cardiac defects and baseline acquired co-morbidities greatly hamper counseling, prognostication and decision-management for neonates with congenital heart defects. We aimed to define the relative risks of these factors. Methods Over 10 years, we have admitted 1618 neonates <30 days with congenital heart defects. For all 1618, consults throughout follow-up were reviewed including genetic consults (318), FISH results (246), cardiology clinics and in-patient progress. Outcomes were analyzed via parametric modeling with multivariate risk-adjusted regression. All risk factors were tested for reliability through bootstrap bagging (N=1000). Results Genetic defects were confirmed in 180 (11%). Aberrations included duplications (98; T21=56, T18=21, T10=13), Ch22 defects (47), Turner (4) and specific gene mutations. An additional 180 (11%) had defined clinical syndromes or congenital non-cardiac defects. Acquired non-cardiac co-morbidity at time of presentation was present in 244 (15%) (CNS=11; Resp=69; GIT=55; Renal=30; Sepsis=70), 144 of whom also had genetic defects. “Perfect” patients - lacking genetic, syndromic or acquired co-morbidites - comprised 1118 (69%). Comfort care was offered to 61 (Perfect=11; T18=21; T13=8; T21=1; Acquired=2). The actively managed 1557 showed late survival >90% for the “perfect” patient. Genetic defects/syndromes and acquired co-morbidity strongly affected survival (figure). However in risk-adjusted analyses, chromosomal/gene defects were overshadowed by acquired morbidity or congenital defects affecting specific systems (table; CNS, Respiratory). Conclusions The “perfect” neonate with congenital hearts disease has an excellent prognosis. However, chromosomal/genetic aberrations do not necessarily imply poor outcome. Instead, acquired non-cardiac morbidity at time of presentation are stronger determinants of outcome.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".