Discovering a new part of the phenotypic spectrum of Coffin-Siris syndrome in a fetal cohort
Bibliographic record
Abstract
PURPOSE: Genome-wide sequencing is increasingly being performed during pregnancy to identify the genetic cause of congenital anomalies. The interpretation of prenatally identified variants can be challenging and is hampered by our often limited knowledge of prenatal phenotypes. To better delineate the prenatal phenotype of Coffin-Siris syndrome (CSS), we collected clinical data from patients with a prenatal phenotype and a pathogenic variant in one of the CSS-associated genes. METHODS: Clinical data was collected through an extensive web-based survey. RESULTS: We included 44 patients with a variant in a CSS-associated gene and a prenatal phenotype; 9 of these patients have been reported before. Prenatal anomalies that were frequently observed in our cohort include hydrocephalus, agenesis of the corpus callosum, hypoplastic left heart syndrome, persistent left vena cava, diaphragmatic hernia, renal agenesis, and intrauterine growth restriction. Anal anomalies were frequently identified after birth in patients with ARID1A variants (6/14, 43%). Interestingly, pathogenic ARID1A variants were much more frequently identified in the current prenatal cohort (16/44, 36%) than in postnatal CSS cohorts (5%-9%). CONCLUSION: Our data shed new light on the prenatal phenotype of patients with pathogenic variants in CSS genes.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".