Prenatal diagnosis and risk stratification of congenital diaphragmatic hernia
Bibliographic record
Abstract
Congenital diaphragmatic hernia (CDH) is a rare heterogenous disorder with varying degrees of severity. Infant survival rates in high-income countries are approaching 80% in isolated CDH; however, over 50% will have long-term morbidities. Advanced antenatal imaging, including ultrasound and magnetic resonance imaging, has made it possible to prognosticate severity of CDH and to stratify risk when counseling expectant parents. Risk stratification can also better prepare healthcare teams to enable optimal neonatal management, and provide options for fetal intervention or, where legally permitted, pregnancy termination. Factors that may affect the immediate and long-term prognosis for CDH include prenatal diagnosis, gestational age at detection and delivery, side of the defect, presence of additional structural or genetic abnormalities, defect size, estimation of fetal lung volume, the extent of visceral herniation, and the delivery center's experience in caring for neonates with CDH. Optimizing the outcome for families and infants begins with an early prenatal diagnosis followed by referral to a diverse and inclusive multidisciplinary center with CDH expertise. Prediction of disease severity is supported by accurate fetal imaging and comprehensive genetic testing, and allows the care team to provide realistic outcome expectations during the counseling of expectant parents of all racial and ethnic backgrounds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".