RADİYAL IŞIN DEFEKTLERİNİN KLİNİK SINIFLANDIRMASI VE ETYOPATOGENEZİNİN ARAŞTIRILMASI
Bibliographic record
Abstract
<!--block-->Objective: Radial ray defects (RRDs) are the most common congenital abnormality of the upper extremities, with a prevalence of 1:30,000. 70% of RRDs are syndromic or accompanied by additional malformations, whereas 30% are in isolated form. Definitive diagnosis is critical for follow-up and provides an opportunity for prenatal diagnosis. The aim of this study was to provide a guide for the differential diagnosis of patients with RRD via contributing to their molecular diagnosis by constructing a next-generation sequencing (NGS) gene-panel test. Materials and Methods: 48 probands from 37 families, referred for genetic consultation due to RRD, between the years of 2004– 2014, were evaluated by cytogenetic and molecular tools following clinical examinations. 31 probands, with normal karyotype, were screened for 43 RRD associated genes of 14 syndromes by using in-house-designed targeted NGS gene-panel. Results: Chromosomal abnormalities [a trisomy 18 and a familial reciprocal translocation t(2;12)(q31;q24.3)] in two families and mutations in related genes (SF3B4, SALL4, TBX5, FANCA) in four families were known before the initiation of this study. In remaining 31 probands, five families identified to have six different mutations in four different genes (FANCA, NIPBL, ESCO2, BRIP1). Conclusion: Chromosomal abnormalities in two of the 37 families (5.4%) and gene mutations in nine of the 37 families (24.3%) were identified. Our study demonstrated that an in-house-designed targeted NGS containing 43 genes made considerable contribution to the diagnosis of RRD. Moreover, chromosomal abnormalities must always be considered in the differential diagnosis and excluded before gene-panel screening.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".