Molecular confirmation of nine cases of Cornelia de Lange syndrome diagnosed prenatally
Bibliographic record
Abstract
OBJECTIVES: Cornelia de Lange syndrome (CdLS) is characterized by distinct facial features, growth retardation, upper limb reduction defects, hirsutism, and intellectual disability. NIPBL mutations have been identified in approximately 60% of patients with CdLS diagnosed postnatally. Prenatal ultrasound findings include upper limb reduction defects, intrauterine growth restriction, and micrognathia. CdLS has also been associated with decreased PAPP-A and increased nuchal translucency (NT). We reviewed NIPBL sequence analysis results for 12 prenatal samples in our laboratory to determine the frequency of mutations in our cohort. METHODS: This retrospective study analyzed data from all 12 prenatal cases with suspected CdLS, which were received by The University of Chicago Genetic Services Laboratories. Diagnostic NIPBL sequencing was performed for all samples. Clinical information was collected from referring physicians. RESULTS: NIPBL mutations were identified in 9 out of the 12 cases prenatally (75%). Amongst the NIPBL mutation-positive cases with clinical information available, the most common findings were upper limb malformations and micrognathia. Five patients had NT measurements in the first trimester, of which four were noted to be increased. CONCLUSION: We demonstrate that prenatally-detected phenotypes of CdLS, particularly severe micrognathia and bilateral upper limb defects, are associated with an increased frequency of NIPBL mutations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".