Prenatal Ultrasound Screening of Congenital Heart Disease in the General Population
Bibliographic record
Abstract
Congenital heart diseases (CHDs) carry a high prevalence rate in the general population (0.8%-1%). Most fetal CHDs occur in patients without any risk factors. The prenatal recognition of CHD has major impacts on the pregnancy and its outcome. The aforementioned data justify prenatal ultrasound (US) screening of CHD in the general low-risk population. As demonstrated in the literature, the application of an extended basic US cardiac examination improves the detection of CHD, in particular the conotruncal anomalies. The stepwise method suggested for fetal heart US screening during the mid-second trimester sonogram is based on 4 routine axial views of heart and great vessels: (1) a transverse view of the superior abdomen, (2) a 4-chamber view, (3) a 3-vessel view, and (4) a transverse view of the aortic arch. This protocol can be obtained rapidly because these scans are easy to perform. Despite the fact that the sequential segmental approach universally used in the postnatal diagnosis of CHD is not specifically addressed here, the detected anomalies can be categorized according to these views, and a short differential diagnosis proposed. Abnormal cardiac and/or vascular landmarks shown on these key scans should lead to a referral in the fetal cardiac center for a more precise evaluation, as well as for counseling.
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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".