The metabolic evaluation of the child with an intellectual developmental disorder: Diagnostic algorithm for identification of treatable causes and new digital resource
Bibliographic record
Abstract
Intellectual developmental disorders (IDD), characterized by significant impairment of cognitive functions, with limitations of learning, adaptive behavior and skills, are frequent (2.5% of the population affected) and present with significant co-morbidity. The burden of IDD, in terms of emotional suffering and associated health care costs, is significant; prevention and treatment therefore are important. A systematic literature review, updated in 2013, identified 89 inborn errors of metabolism (IEMs), which present with IDD as prominent feature and are amenable to causal therapy. Therapeutic effects include improvement and/or stabilization of psychomotor/cognitive development, behavior/psychiatric disturbances, seizures, neurologic and systemic manifestations. The levels of available evidence for the various treatments range from Level 1b, c (n=5); Level 2a, b, c (n=14); Level 4 (n=53), and Levels 4-5 (n=27). For a target audience comprising clinical and biochemical geneticists, child neurologists and developmental pediatricians, five experts translated....this data into a 2-tiered diagnostic algorithm: The first tier comprises metabolic "screening" tests in urine and blood, which are relatively accessible, affordable, less invasive, and have the potential to identify 60% of all treatable IEMs. The second tier investigations for the remaining disorders are ordered based on individual clinical signs and symptoms. This algorithm is supported by an App www.treatable-id.org, which comprises up-to-date information on all 89 IEMs, relevant diagnostic tests, therapies and a search function based on signs and symptoms. These recommendations support the clinician in early identification of treatable IEMs in the child with IDD, allowing for timely initiation of therapy with the potential to improve neurodevelopmental outcomes. The need for future studies to determine yield and usefulness of these recommendations, with subsequent updates and improvements to developments in the field, is outlined.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".