Whole‐genome array CGH identifies pathogenic copy number variations in fetuses with major malformations and a normal karyotype
Bibliographic record
Abstract
Despite a wide range of clinical tools, the etiology of mental retardation and multiple congenital malformations remains unknown for many patients. Array-based comparative genomic hybridization (aCGH) has proven to be a valuable tool in these cases, as its pangenomic coverage allows the identification of chromosomal aberrations that are undetectable by other genetic methods targeting specific genomic regions. Therefore, aCGH is increasingly used in clinical genetics, both in the postnatal and the prenatal settings. While the diagnostic yield in the postnatal population has been established at 10-12%, studies investigating fetuses have reported variable results. We used whole-genome aCGH to investigate fetuses presenting at least one major malformation detected on ultrasound, but for whom standard genetic analyses (including karyotype) failed to provide a diagnosis. We identified a clinically significant chromosomal aberration in 8.2% of tested fetuses (4/49), and a result of unclear clinical significance in 12.2% of tested fetuses (6/49). Our results document the value of whole-genome aCGH as a prenatal diagnostic tool and highlight the interpretation difficulties associated with copy number variations of unclear significance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".