Identification of a prenatal profile of Cornelia de Lange syndrome (CdLS): A review of 53 CdLS pregnancies
Bibliographic record
Abstract
Cornelia de Lange Syndrome (CdLS) is a multisystem developmental disorder characterized by growth retardation, cognitive impairment, external and internal structural malformations, and characteristic facial features. Currently, there are no definitive prenatal screening measures that lead to the diagnosis of CdLS. In this study, documented prenatal findings in CdLS syndrome were analyzed towards the development of a prenatal profile predictive of CdLS. We reviewed 53 cases of CdLS (29 previously reported and 24 unreported) in which prenatal observations/findings were available. The review of these cases revealed a pattern of sonographic findings, including obvious associated structural defects, growth restriction, as well as a more subtle, but strikingly characteristic, facial profile, and suggestive of a recognizable prenatal ultrasonographic profile for CdLS. In addition, the maternal serum marker, PAPP-A, may be reduced and fetal nuchal translucency (NT) may be increased in some pregnancies when measured at an appropriate gestational age. In conclusion, CdLS can be prenatally diagnosed or readily ruled out in a family with a known mutation in a CdLS gene. The characteristic ultrasonographic profile may allow for prenatal diagnosis of CdLS in (1) subsequent pregnancies to a couple with a prior child with CdLS in whom a mutation has not been identified or (2) when there are unexplained pregnancy signs of fetal abnormality, such as oligo- or polyhydramnios, a low maternal serum PAPP-A level and/or increased NT, fetal growth retardation, or structural anomalies consistent with CdLS.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".