Biochemical and molecular predictors for prognosis in nonketotic hyperglycinemia
Bibliographic record
Abstract
OBJECTIVE: Nonketotic hyperglycinemia is a neurometabolic disorder characterized by intellectual disability, seizures, and spasticity. Patients with attenuated nonketotic hyperglycinemia make variable developmental progress. Predictive factors have not been systematically assessed. METHODS: We reviewed 124 patients stratified by developmental outcome for biochemical and molecular predictive factors. Missense mutations were expressed to quantify residual activity using a new assay. RESULTS: Patients with severe nonketotic hyperglycinemia required multiple anticonvulsants, whereas patients with developmental quotient (DQ) > 30 did not require anticonvulsants. Brain malformations occurred mainly in patients with severe nonketotic hyperglycinemia (71%) but rarely in patients with attenuated nonketotic hyperglycinemia (7.5%). Neonatal presentation did not correlate with outcome, but age at onset ≥ 4 months was associated with attenuated nonketotic hyperglycinemia. Cerebrospinal fluid (CSF) glycine levels and CSF:plasma glycine ratio correlated inversely with DQ; CSF glycine > 230 μM indicated severe outcome and CSF:plasma glycine ratio ≤ 0.08 predicted attenuated outcome. The glycine index correlated strongly with outcome. Molecular analysis identified 99% of mutant alleles, including 96 novel mutations. Mutations near the active cleft of the P-protein maintained stable protein levels. Presence of 1 mutation with residual activity was necessary but not sufficient for attenuated outcome; 2 such mutations conferred best outcome. Divergent outcomes for the same genotype indicate a contribution of other genetic or nongenetic factors. INTERPRETATION: Accurate prediction of outcome is possible in most patients. A combination of 4 factors available neonatally predicted 78% of severe and 49% of attenuated patients, and a score based on mutation severity predicted outcome with 70% sensitivity and 97% specificity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".