THE YIELD OF A UNIFIED SCREEN FOR THE INVESTIGATION OF INBORN ERRORS OF METABOLISM IN CHILDREN WITH NEURODEVELOPMENTAL CONDITIONS – A RETROSPECTIVE CHART REVIEW
Bibliographic record
Abstract
Abstract BACKGROUND Neurodevelopmental conditions (NDCs) such as Global Developmental Delay (GDD), Intellectual Disability (ID) and Autism Spectrum Disorders (ASD), are clinically heterogenous. Many of their etiologies are genetic. Previous studies report differing detection rates of inborn errors of metabolism (IEM) as causes of NDCs. Small sample sizes, poorly defined populations and variable diagnostic approaches limit these studies. Improved detection and treatment of IEM demands a better understanding of the diagnostic yield of the investigation of IEM as an etiology of NDCs. This will help establish a streamlined process in the diagnostic evaluation NDCs. OBJECTIVES The objective of this project is to establish the detection rate of IEM in a well-characterized cohort of children with NDCs, investigated for IEM with a unified screening test panel. This study was approved by the Research Ethics Board. DESIGN/METHODS This is a retrospective medical chart review of children with NDCs (GDD, ID and/or ASD), aged 0 to 18 years, diagnosed at a Canadian quaternary center. Charts were collected between March 2015 and 2016. The children were seen for a diagnostic work-up in Developmental Paediatrics, Neurology or Genetics clinics. As part of the standard investigation these services use a unified test panel for screening for IEM. Data was collected on the yield of metabolic testing. RESULTS Out of 188 children with NDCs fulfilling the inclusion criteria, 115 (61.1%) underwent the complete IEM screening panel, 52 (27.6%) had partial work-up and 21 (11.2%) did not undergo tests for IEM. The average age of the children was 5 years. Children had the following: ASD (104, 55.3%), GDD (68, 36.2%), ID (10, 5.3%), ASD and ID (6, 3.2%). The diagnostic yield for IEM in those who underwent the entire IEM screening panel was 2.6% (3 confirmed IEM). Three additional children have results suspicious for IEM, but are awaiting confirmatory testing. This suggests that the yield may be as high as 5.2%. Two (3.8%) children with partial IEM work-up also have abnormal metabolic results but diagnoses of IEM are pending. CONCLUSION The detection rate of IEM in the children with NDCs, who underwent screening with a unified test panel, was 2.6%. However, given that confirmatory investigations for other children were pending at the time of our study, the actual yield is likely higher. Therefore, IEM should be considered as an etiology in the routine investigation of NDCs. Screening for IEM should be done in a systematic manner.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".