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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".