Fetal Diagnosis of Hypoplastic Left Heart Syndrome With Restrictive Atrial Septum—Atrial Septal Morphology, Associated Lung Disease and Outcomes
Bibliographic record
Abstract
OBJECTIVE: Fetuses with hypoplastic left heart syndrome (HLHS) and restrictive/intact atrial septum (RAS) have high mortality, partly due to pulmonary lymphangiectasia (PL). This study aimed to characterize atrial septal morphology in fetuses with HLHS and RAS and evaluate the impact of fetal intervention on PL and outcomes. METHOD: We retrospectively reviewed clinical data, fetal echocardiograms, and MRIs from all fetuses with HLHS/HLHS-variants and RAS over 20 years. RAS was defined as pulmonary venous forward-to-reverse VTI ratio ≤ 5:1. RESULTS: We identified 54 fetuses (gestational age of 24.2 weeks; 21.2-28.1) with HLHS (81%) or HLHS variant (19%) and RAS. Most had a hypoplastic left atrium (LA, 80%). Four atrial septal morphology patterns were identified. PL was present among all patterns and associated with lower VTI ratio (p = 0.046). Mean VTI ratio improved in fetuses who underwent atrial septal intervention (n = 12), compared to those without (p = 0.001). Among five fetuses with pre-intervention PL and serial MRIs, three improved. Seventeen neonates were live-born with intention-to-treat; 6-months mortality was 38% with no difference by morphology pattern, PL, or fetal intervention. CONCLUSION: Most fetuses with HLHS and RAS had hypoplastic LA. VTI ratio correlated with PL on fetal lung MRI, and most improved with fetal atrial septal intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".