Whole‐genome array CGH identifies pathogenic copy number variations in fetuses with major malformations and a normal karyotype
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it