Prenatal cardiac findings and 22q11.2 deletion syndrome: Fetal detection and evaluation
Bibliographic record
Abstract
Clinical features of 22q11.2 microdeletion syndrome (22q11.2DS) are highly variable between affected individuals and frequently include a subset of conotruncal and aortic arch anomalies. Many are diagnosed with 22q11.2DS when they present as a fetus, newborn or infant with characteristic cardiac findings and subsequently undergo genetic testing. The presence of an aortic arch anomaly with characteristic intracardiac anomalies increases the likelihood that the patient has 22q11.2 DS, but those with an aortic arch anomaly and normal intracardiac anatomy are also at risk. It is particularly important to identify the fetus at risk for 22q11.2DS in order to prepare the expectant parents and plan postnatal care for optimal outcomes. Fetal anatomy scans now readily identify aortic arch anomalies (aberrant right subclavian artery, right sided aortic arch or double aortic arch) in the three-vessel tracheal view. Given the association of 22q11.2DS with aortic arch anomalies with and without intracardiac defects, this review highlights the importance of recognizing the fetus at risk for 22q11.2 deletion syndrome with an aortic arch anomaly and details current methods for genetic testing. To assist in the prenatal diagnosis of 22q11.2DS, this review summarizes the seminal features of 22q11.2DS, its prenatal presentation and current methods for genetic testing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
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".