Smith–Magenis syndrome and moyamoya disease in a patient with del(17)(p11.2p13.1)
Bibliographic record
Abstract
Chromosomal rearrangements causing microdeletions and microduplications are a major cause of congenital malformation and mental retardation. Because they are not visible by routine chromosome analysis, high resolution whole-genome technologies are required for the detection and diagnosis of small chromosomal abnormalities. Recently, array-comparative genomic hybridization (aCGH) and multiplex ligation-dependent probe amplification (MLPA) have been useful tools for the identification and mapping of deletions and duplications at higher resolution and throughput. Smith-Magenis syndrome (SMS) is a multiple congenital anomalies/mental retardation syndrome caused by deletion or mutation of the retinoic acid induced 1 (RAI1) gene and is often associated with a chromosome 17p11.2 deletion. We report here on the clinical and molecular analysis of a 10-year-old girl with SMS and moyamoya disease (occlusion of the circle of Willis). We have employed a combination of aCGH, FISH, and MLPA to characterize an approximately 6.3 Mb deletion spanning chromosome region 17p11.2-p13.1 in this patient, with the proximal breakpoint within the RAI1 gene. Further, investigation of the genomic architecture at the breakpoint intervals of this large deletion documented the presence of palindromic repeat elements that could potentially form recombination substrates leading to unequal crossover.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".