Guanidinoacetate methyltransferase (GAMT) deficiency: Outcomes in 48 individuals and recommendations for diagnosis, treatment and monitoring
Bibliographic record
Abstract
We collected data on 48 patients from 38 families with guanidinoacetate methyltransferase (GAMT) deficiency. Global developmental delay/intellectual disability (DD/ID) with speech/language delay and behavioral problems as the most affected domains was present in 44 participants, with additional epilepsy present in 35 and movement disorder in 13. Treatment regimens included various combinations/dosages of creatine-monohydrate, l-ornithine, sodium benzoate and protein/arginine restricted diets. The median age at treatment initiation was 25.5 and 39 months in patients with mild and moderate DD/ID, respectively, and 11 years in patients with severe DD/ID. Increase of cerebral creatine and decrease of plasma/CSF guanidinoacetate levels were achieved by supplementation with creatine-monohydrate combined with high dosages of l-ornithine and/or an arginine-restricted diet (250 mg/kg/d l-arginine). Therapy was associated with improvement or stabilization of symptoms in all of the symptomatic cases. The 4 patients treated younger than 9 months had normal or almost normal developmental outcomes. One with inconsistent compliance had a borderline IQ at age 8.6 years. An observational GAMT database will be essential to identify the best treatment to reduce plasma guanidinoacetate levels and improve long-term outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".