Further delineation of cardio-facio-cutaneous syndrome: clinical features of 38 individuals with proven mutations
Bibliographic record
Abstract
BACKGROUND: Cardio-facio-cutaneous syndrome (CFC) is a multiple congenital anomaly/mental retardation syndrome named because of a characteristic facies, cardiac anomalies, and ectodermal abnormalities. While considerable literature describes the main features, few studies have documented the frequencies of less common features allowing a greater appreciation of the full phenotype. METHODS: We have analysed clinical data on 38 individuals with CFC and a confirmed mutation in one of the genes known to cause the condition. We provide data on well-established features, and those that are less often described. RESULTS: Polyhydramnios (77%) and prematurity (49%) were common perinatal issues. 71% of individuals had a cardiac anomaly, the most common being pulmonary valve stenosis (42%), hypertrophic cardiomyopathy (39%), and atrial septal defect (28%). Hair anomalies were also typical: 92% had curly hair, 84% sparse hair, and 86% absent or sparse eyebrows. The most frequent cutaneous features were keratosis pilaris (73%), hyperkeratosis (61%) and nevi (76%). Significant and long lived gastrointestinal dysmotility (71%), seizures (49%), optic nerve hypoplasia (30%) and renal anomalies, chiefly hydronephrosis (20%), were among the less well known issues reported. CONCLUSION: This study reports a broad range of clinical issues in a large cohort of individuals with molecular confirmation of CFC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".