Characterization of patients referred for non‐specific intellectual disability testing: the importance of autosomal genes for diagnosis
Bibliographic record
Abstract
Genetic testing for non-specific intellectual disability (ID) presents challenges in daily clinical practice. Historically, the focus of the genetic elucidation of non-specific ID has been on genes on the X chromosome, and recent research has brought attention to the growing contribution of autosomal genes. In addition, next-generation sequencing (NGS) has greatly improved the ability to simultaneously analyze multiple genetic loci, making large panel testing a practical approach to testing for non-specific ID. We performed NGS analysis of a total of 90 genes implicated in non-specific ID. The 90 genes included 56 X-linked genes and 34 autosomal genes. Pathogenic variants were identified in 11 of 52 (21%) patient samples. Nine of the eleven cases harbored mutations in autosomal genes including AP4B1, STXB1, SYNGAP1, TCF4 and UBE3A. Our mutation-positive cases provide further evidence supporting the prevalence of autosomal mutations in patients referred for non-specific ID testing and the utility of their inclusion in multi-gene panel analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".