Urine Sorbitol and Xylitol for the Diagnosis of Sorbitol Dehydrogenase Deficiency–Related Neuropathy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: is complicated by the presence of a pseudogene. Biochemical testing of serum sorbitol is suggested as a potential biomarker. We report a novel urine biochemical profile of elevated excretion of sorbitol and a second polyol, xylitol, to aid in the identification of individuals with SORD-related neuropathy. METHODS: Patients with confirmed or suspected SORD-related neuropathy were recruited by clinicians from 7 academic medical centers and 1 nonprofit specialty care center. Urine was analyzed by a clinically validated, gas chromatography-mass spectrometry assay to measure sorbitol and xylitol excretion. Over 700 reference samples were evaluated from residual clinical samples. Clinical and molecular findings were gathered using a standardized questionnaire. RESULTS: variant detected, whereas normal urine sorbitol/xylitol levels were observed in 11 asymptomatic, heterozygous carriers. DISCUSSION: These data demonstrate the clinical utility of urine sorbitol and xylitol analysis in the screening and diagnosis of SORD deficiency-related neuropathy. In addition, this is the first description of elevated xylitol in SORD deficiency as a clinically informative biomarker, which may increase the specificity of biochemical testing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".