A Report of Two Families with Sarcosinaemia in Hong Kong and Revisiting the Pathogenetic Potential of Hypersarcosinaemia
Bibliographic record
Abstract
INTRODUCTION: Sarcosinaemia is a rare metabolic disorder which has not been reported in Asia. CLINICAL PICTURE: The urine samples of 2 patients were screened as a routine metabolic screening offered for patients with mental retardation in our hospital. We used gas chromatography-mass spectrometry (GC-MS) which is capable of detecting abnormal pattern in amino acids and organic acids. Plasma sarcosine level was further quantified by GC-MS. The same methods were used in the investigations of asymptomatic family members. Urine examination by GC-MS revealed excessive amount of sarcosine in urine (normally undetectable) and their plasma sarcosine levels were raised. The 2 differential diagnoses of presence of sarcosine in urine--glutaric aciduria type II and folate deficiency--were ruled out by the absence of abnormal organic acids in the initial urine screen and by normal serum folate level respectively. Screening of the 2 families identified excessive sarcosine in urine in 2 siblings, one from each family. However, these 2 siblings of indexed patients thus identified have no neurological or developmental problem. CONCLUSION: Our finding was consistent with the notion that sarcosinaemia is a benign condition picked up coincidentally during screening for mental retardation.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".