Predicting High-Risk Fetal Cardiac Disease Anticipated to Need Immediate Postnatal Stabilization and Intervention with Planned Pediatric Cardiac Operating Room Delivery
Bibliographic record
Abstract
ABSTRACT Background Distances between delivery centers and cardiac services can make the care of fetuses with cardiac disease(CD) at risk of acute cardiorespiratory instability(ACRI) at birth a challenge. In 2013 we implemented a fetal echocardiography(FE)-based algorithm targeting fetuses considered high-risk for ACRI at ≤2 hours of birth for Caesarian section(CS) delivery in our pediatric cardiac operating room(PCOR) of our children’s hospital. We examine the experience and outcomes of affected newborns. Methods We reviewed maternal and postnatal medical records of all fetuses with CD at high-risk for ACRI encountered January 2013-March 2022. Secondary analysis was performed including all fetuses with diagnoses of d-transposition of the great arteries/intact ventricular septum(d-TGA/IVS) and hypoplastic left heart syndrome(HLHS) encountered over the study period. Results Forty fetuses were considered high-risk for ACRI: 15 d-TGA/IVS and 7 HLHS with restrictive atrial septum(RAS), 4 absent pulmonary valve syndrome, 3 obstructed anomalous pulmonary veins, 2 severe Ebstein anomaly, 2 thoracic/intracardiac tumors and 7 others. PCOR delivery occurred for 33 but not for 7 (5 d-TGA/IVS, 2 HLHS with RAS). For high-risk cases, FE had a positive predictive value of 50% for intervention/ECMO/death at ≤2 hours and 70% at ≤24 hours. Of “low-risk” cases, 6/46 with d-TGA/IVS and 0/45 with HLHS required intervention at ≤2 hours. FE predicted intervention/ECMO/death at ≤2hours with a sensitivity of 67%, specificity 93%, and positive and negative predictive values of 87% and 87%, respectively, for d-TGA/IVS, and 100%, 95%, 71%, and 100% for HLHS, respectively. Conclusions FE predicts need for urgent intervention in majority with d-TGA/IVS and HLHS, and in half of the entire spectrum of high-risk CD.
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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 | 0.001 | 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".