Inherited Pediatric Neurotransmitter Disorders: Case Studies and Long-Term Outcomes
Bibliographic record
Abstract
Abstract Primary pediatric neurotransmitter disorders include genetic defects of neurotransmitter metabolism that may mimic common neurological conditions in children. Our objective was to evaluate the clinical experience and outcomes of affected patients. Five patients with primary neurotransmitter defects were identified in the neurometabolic database between 2004 and 2022. Two patients with 6-pyruvoyltetrahydropterin synthase deficiency and one with pyridoxine-dependent epilepsy (PDE) presented in the neonatal period. One patient with succinic semialdehyde dehydrogenase (SSADH) deficiency and one with aromatic l-amino acid decarboxylase (AADC) deficiency presented in later life. A diagnosis of cerebral palsy was revised following biochemical confirmation of SSADH deficiency. AADC deficiency was confirmed via exome sequencing and reduced activity on enzyme assay. Late diagnosis in the latter two cases was likely due to a low index of suspicion and lack of access to diagnostic tests in the country of origin. In two children with tetrahydrobiopterin deficiency, newborn screening results and atypical clinical features prompted investigations. An early diagnosis of PDE was established based on presenting features, a high index of suspicion, the presence of an identifiable biochemical marker and molecular genetic testing. Pediatric neurotransmitter disorders can be diagnosed based on a high clinical index of suspicion, availability of biochemical markers, and molecular genetic testing. These disorders, though rare, need to be included in the differential diagnosis of common neurological presentations in children as they may be potentially treatable. Outcomes and influencing factors in the present series are discussed in comparison to published data.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".