Pitfalls in Genetic Testing for Consanguineous Pediatric Populations
Bibliographic record
Abstract
We describe the diagnostic odyssey of an eight-year-old female born to consanguineous parents. Our patient presented with global developmental delay, regression, microcephaly, spastic diplegia, and leukodystrophy confirmed on brain magnetic resonance imaging (MRI). She was found on whole exome sequencing (WES) to have dual genetic diagnoses. The first was a homozygous pathogenic HERC2 gene partial deletion of exons 43-45 that causes HERC2-related disorder. The second was a homozygous pathogenic variant (c.836 C > T, p.A279 V) in the SUMF1 gene responsible for multiple sulfatase deficiency. This case highlights some of the challenges in diagnosing consanguineous pediatric populations where standard genetic and metabolic testing may not provide answers. Our case further supports the recent American College of Medical Genetics and Genomics (ACMG) recommendation of WES as a first or second-tier test for patients with developmental delay, particularly in a population where the chances of dual diagnosis is high.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".