Monogenic conditions and central nervous system anomalies: A prospective study, systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objectives Determine the incremental diagnostic yield of prenatal exome sequencing (pES) over chromosome microarray (CMA) or G‐banding karyotype in fetuses with central nervous system (CNS) abnormalities. Methods Data were collected via electronic searches from January 2010 to April 2022 in MEDLINE, Cochrane, Web of Science and EMBASE. The NHS England prenatal exome cohort was also included. Incremental yield was calculated as a pooled value using a random‐effects model. Results Thirty studies were included ( n = 1583 cases). The incremental yield with pES for any CNS anomaly was 32% [95%CI 27%–36%; I 2 = 72%]. Subgroup analysis revealed apparent incremental yields in; (a) isolated CNS anomalies; 27% [95%CI 19%–34%; I 2 = 74%]; (b) single CNS anomaly; 16% [95% CI 10%–23%; I 2 = 41%]; (c) more than one CNS anomaly; 31% [95% Cl 21%–40%; I 2 = 56%]; and (d) the anatomical subtype with the most optimal yield was Type 1 malformation of cortical development, related to abnormal cell proliferation or apoptosis, incorporating microcephalies, megalencephalies and dysplasia; 40% (22%–57%; I 2 = 68%). The commonest syndromes in isolated cases were Lissencephaly 3 and X‐linked hydrocephalus. Conclusions Prenatal exome sequencing provides a high incremental diagnostic yield in fetuses with CNS abnormalities with optimal yields in cases with multiple CNS anomalies, particularly those affecting the midline, posterior fossa and cortex.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".