Diagnostic yield from routine metabolic screening tests in evaluation of global developmental delay and intellectual disability
Bibliographic record
Abstract
Global developmental delay and intellectual disability (GDD/ID) affect 3% of the paediatric population. Although inborn errors of metabolism (IEM) are not a common cause of GDD/ID, early therapeutic intervention can improve neurodevelopmental manifestations. In 2012, a first-tier test panel, including specialized metabolic and routine chemistry tests, was piloted to community-based paediatricians in British Columbia with aims to achieve earlier diagnosis of treatable IEM. OBJECTIVE: The aim of this retrospective review was to evaluate the diagnostic yield from these first-tier tests in the 7 years before (2006 to 2012) and after (2013 to 2019) implementation at the community paediatrician level. RESULTS: Prior and postimplementation diagnostic yield of an IEM from first-tier metabolic testing was 9 out of 986 (0.91%) and 11 out of 4,345 children (0.25%), respectively. Disorders of creatine metabolism and organic acidurias were the most frequently established diagnoses in both time periods. No diagnoses were established through acylcarnitine copper/ceruloplasmin, lactate, or ammonia testing. Twenty out of 24 patients had specific neurological or other red flag signs in addition to GDD/ID. Four boys diagnosed with an x-linked creatine transporter defect (CTD) had speech-language delay as the most prominent finding. CONCLUSIONS: The expansion of first-tier metabolic testing to community-based paediatricians in BC did not yield an increase in IEM diagnoses. A modified first-tier test panel should be offered to patients with GDD/ID, neurologic, and/or red flag signs. Urine creatine testing in boys with speech-language delay warrants consideration to detect CTD.
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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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".