Prenatal detection of subtelomeric rearrangements by multi‐subtelomere FISH in a cohort of fetuses with major malformations
Bibliographic record
Abstract
Cryptic unbalanced subtelomeric rearrangements have been identified as an important contributor ( approximately 6%) to the etiology of mental retardation and dysmorphism. Our objective was to study the role of these rearrangements in the development of fetal malformations. Multi-subtelomere FISH was performed on cells from 48 fetuses with major malformations diagnosed by prenatal ultrasound with a normal karyotype at a minimal 400 band resolution. We developed a method of performing multi-subtelomere FISH on a single slide of amniocyte metaphase spreads. We identified five subtelomeric abnormalities: two derivative chromosomes inherited from a parent carrying a balanced translocation, two known polymorphisms, and one novel familial variant. These results show a similar frequency (4%) of clinically significant subtelomeric rearrangements to that found in children with multiple malformations. This study adds to a growing number of reports of cryptic subtelomeric rearrangements associated with congenital malformations and highlights the relevance and technical feasibility of multi-subtelomere FISH screening of prenatal samples.
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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 | 0.000 |
| 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 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".