Array comparative genomic hybridization in global developmental delay
Bibliographic record
Abstract
OBJECTIVE: Array-based comparative genomic hybridization (array CGH) is an emerging technology that allows for the genome-wide detection of DNA copy number changes (CNC) such as deletions or duplications. In this study, array-based CGH was applied to a consecutive series of children with previously undiagnosed non-syndromal global developmental delay (GDD) to assess potential etiologic yield. METHODS: The children in this study were drawn from a previously reported consecutive series of children with well-defined GDD. Almost all subjects had undergone prior karyotyping and neuroimaging studies with non-diagnostic results. Array-based CGH was undertaken using the SignatureChip(R) (1887 BACs representing 622 loci) with abnormalities verified by subsequent FISH analysis and testing of parents to distinguish between pathogenic and familial non-pathogenic variants. RESULTS: On CGH analysis in our study, 6 of 94 children (6.4%) had a causally related pathogenic CNC. Three were sub-telomeric in location. An analysis of a variety of clinical factors revealed that only the presence of minor dysmorphic features (<3) was predictive of etiologic yield on CGH analysis (4/26 vs. 2/68, P = 0.05). Severity of delay was not found to be predictive. INTERPRETATION: In children with non-syndromal GDD, array-based CGH has an etiologic yield of 6.4%. This suggests that this emerging technology may be of diagnostic value when applied subsequent to detailed history, physical examination, and targeted laboratory testing. Array CGH may merit consideration as a first-tier test in the context of a child with unexplained GDD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".