MG-127 Diagnostic accuracy of chromosome microarray in children with epilepsy and neurological abnormalities of unknown aetiology
Bibliographic record
Abstract
Background Chromosome MicroArray-based genomic copy-number analysis (CMA) has an important role in the discovery of both novel and recurrent epilepsy-associated copy number variants (CNVs) in patients with epilepsy. In case of an additional neuro-developmental disorder the diagnostic accuracy may be as high as 15%. Objectives The purpose of this study is to describe the results of performed CMA on 706 children with unexplained epilepsy associated with developmental delay/intellectual disability, autism spectrum disorders and/or multiple congenital anomalies (‘epilepsy plus’). Design/method Retrospective chart review on clinical and genetic aspects of CNVs identified in 706 patients with ‘epilepsy plus’, seen at the Vancouver BC Children’s Hospital from 2009 to 2014. All patients had CMA performed using Affymetrix Genome-Wide Human SNP Array 6.0 or CytoScanHD®. Results Abnormal CMA results were identified in 191 out of 706 children with ‘epilepsy plus’. 122/706 (17.3%) patients had variants of unknown significance (VUS), and 80/706 (11.3%) patients had pathogenic CNVs. This group included 19 patients that had CNVs in genomic “hotspots” predisposing to epilepsy, including 1q21.1 (n = 1), 15q11.2 (n = 2), 15q13.3 (n = 4), 15q11-q13 (n = 3), 16p11.2 (n = 1046), and 16p13.11 (n = 3). Among other known microdeletion/microduplication syndromes (n = 20) was 22q13.3 deletion (Phelan-McDermid Syndrome) (n = 4). One of the four 22q13.3 deletion patients had treatment resistant epilepsy and a small deletion (47kb) of the SHANK3 gene. Conclusions CMA revealed pathogenic CNVs in epilepsy “hotspots”, and known microdeletion syndromes like Phelan-McDermid syndrome in 11.3% of children with ‘epilepsy plus’. Examination of CNVs also plays an important role in the identification of possible epilepsy genes i.g. SHANK3 mutations in patients with 22q13.3 deletions.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".