Lysosphingolipid Urine Screening Test using Mass Spectrometry for the Early Detection of Lysosomal Storage Disorders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Sphingolipidoses are caused by a defective sphingolipid catabolism, leading to an accumulation of several glycolipid species in tissues and resulting in neurotoxicity and severe systemic manifestations. Methods & results: Urine samples from controls and patients were purified by solid-phase extraction prior to the analysis by ultra-high-performance liquid chromatography (UPLC) combined with MS/MS. A UPLC–MS/MS method for the analysis of 21 urinary creatinine-normalized biomarkers for eight diseases was developed and validated. Conclusion: Considering the growing demand to identify patients with different sphingolipidoses early and reliably, this methodology will be applied for high-risk screening to target efficiently patients with various sphingolipidoses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it