Integrative Approach to Predict Severity in Nonketotic Hyperglycinemia
Bibliographic record
Abstract
OBJECTIVE: Glycine encephalopathy, also known as nonketotic hyperglycinemia (NKH), is an inherited neurometabolic disorder with variable clinical course and severity, ranging from infantile epileptic encephalopathy to psychiatric disorders. A precise phenotypic characterization and an evaluation of predictive approaches are needed. METHODS: Longitudinal clinical and biochemical data of 25 individuals with NKH from the patient registry of the International Working Group on Neurotransmitter Related Disorders were studied with in silico analyses, pathogenicity scores, and molecular modeling of GLDC and AMT variants. RESULTS: Symptom onset (p < 0.01) and diagnosis occur earlier in life in severe NKH (p < 0.01). Presenting symptoms affect the age at diagnosis. Psychiatric problems occur predominantly in attenuated NKH. Onset age ≥ 3 months (66% specificity, 100% sensitivity, area under the curve [AUC] = 0.87) and cerebrospinal fluid (CSF)/plasma glycine ratio ≤ 0.09 (57% specificity, 100% sensitivity, AUC = 0.88) are sensitive indicators for attenuated NKH, whereas CSF glycine concentration ≥ 116.5μmol/l (100% specificity, 93% sensitivity, AUC = 0.97) and CSF/plasma glycine ratio ≥ 0.15 (100% specificity, 64% sensitivity, AUC = 0.88) are specific for severe forms. A ratio threshold of 0.128 discriminates the overlapping range. We present 10 new GLDC variants. Two mild variants resulted in attenuated, whereas 2 severe variants or 1 mild and 1 severe variant led to severe phenotype. Based on clinical, biochemical, and genetic parameters, we propose a severity prediction model. INTERPRETATION: This study widens the phenotypic spectrum of attenuated NKH and expands the number of pathogenic variants. The multiparametric approach provides a promising tool to predict disease severity, helping to improve clinical management strategies. ANN NEUROL 2022;92:292-303.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".