The diagnostic yield of genetic and metabolic investigations in syndromic and nonsyndromic patients with autism spectrum disorder, global developmental delay, or intellectual disability from a dedicated neurodevelopmental disorders genetics clinic
Bibliographic record
Abstract
First-tier genetic investigations for patients with neurodevelopmental disorders (NDDs) may include chromosomal microarray, Fragile X testing, and screening for inherited metabolic diseases, but most remain undiagnosed upon completion of testing. Here, we report the diagnostic yields of genetic testing for 537 patients with at least one of autism spectrum disorder, global developmental delay, and/or intellectual disability. Patients were assessed in a single neurodevelopmental genetics clinic, and each underwent a standardized history and physical examination. Each patient was characterized as syndromic or nonsyndromic based on clinical features. Our results demonstrate that multigene sequencing (with an NDD gene panel or exome) had a higher diagnostic yield (8%; 95% confidence interval [CI]: 5%, 13%) than chromosomal microarray and Fragile X testing combined (4%; 95% CI: 3%, 7%). Biochemical screening for inherited metabolic diseases had a diagnostic yield of zero. The diagnostic yield of genetic testing was significantly higher for syndromic patients than for nonsyndromic patients (odds ratio [OR] 3.09; 95% CI: 1.46, 6.83) and higher for female patients than for male (OR 3.21; 95% CI: 1.52, 6.82). These results add to the growing evidence supporting a comprehensive genetic evaluation that includes both copy number analysis and sequencing of known NDD genes for patients with NDDs.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".