Prenatal diagnosis for arginase deficiency: A case study
Bibliographic record
Abstract
Arginase deficiency is a rare, autosomal recessive, disorder of the urea cycle characterized by mild hyperammonaemia, hyperargininaemia, dibasic aminoaciduria and orotic aciduria, associated with progressive spastic tetraplegia, seizures, psychomotor retardation, and growth failure. We report a family who presented with their daughter at 4 years 11 months of age with an acute encephalopathy. Initial laboratory results revealed hyperammonaemia (160 micromol/L; normal 0-34), hyperargininaemia (512 micromol/L; normal 23-86) and orotic aciduria. A diagnosis of arginase deficiency was confirmed by enzyme assay, and treatment with a modified protein-restricted diet along with sodium benzoate therapy was initiated. Over time, intellectual development has been normal, but the child developed spasticity in her lower extremities. Subsequently, the mother presented at 6 weeks of pregnancy seeking prenatal diagnosis. Prenatal testing for arginase deficiency has only been reported in one other case. Arginase is not expressed in cultured amniotic fluid cells or chorionic villus samples. Testing for arginase activity assay in red blood cells, isolated by cordocentesis, was performed and predicted an unaffected fetus. The result was confirmed by postnatal enzyme analysis of red cells from the newborn. On the basis of our experience, prenatal diagnosis of arginase deficiency by cord red blood cell arginase activity assay appears possible.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".