Serum neurofilament light chain is not a useful biomarker of central nervous system involvement in women with Fabry disease
Bibliographic record
Abstract
Neurofilament Light Chain (NfL) serum concentration is a new noninvasive marker of neurodegenerative disorders. Fabry disease (FD) leads to accumulation of glycosphingolipids in tissues leading to progressive damage of critical body systems and organs, including peripheral and central nervous system. There are no established serum markers of neurodegeneration in FD. Our cross-sectional single-center study was designed to prove the concept that serum NfL levels could reflect the severity of cognitive impairment and indirectly, the level of central nervous system involvement in women at earlier stages of FD. Twelve women with a diagnosis of FD confirmed by genetic tests and 12 matched healthy subjects were included. Serum concentrations of NfL were measured in all subjects together with neuropsychological tests that included Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA). Quality of life was assessed with the Short Form Survey (SF-36). FD patients and healthy subjects did not differ with respect to serum NfL concentration, results of neuropsychological tests and quality of life. There was a significant positive correlation between NfL and globotriaosylosphingosine (lyso-Gb3) concentration in women with FD (R = 0,69, p = 0.01). There was also a correlation between NfL concentration and MoCA score but not MMSE score. Receiver operating characteristic (ROC) analysis showed that the best predictor for Mild Cognitive Impairment in both groups was eGFR. Serum NfL concentration does not appear to predict the degree of nervous system involvement in women with FD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".