The potential of serum neurofilament as biomarker for multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis is a highly heterogeneous disease, and the detection of neuroaxonal damage as well as its quantification is a critical step for patients. Blood-based serum neurofilament light chain (sNfL) is currently under close investigation as an easily accessible biomarker of prognosis and treatment response in patients with multiple sclerosis. There is abundant evidence that sNfL levels reflect ongoing inflammatory-driven neuroaxonal damage (e.g. relapses or MRI disease activity) and that sNfL levels predict disease activity over the next few years. In contrast, the association of sNfL with long-term clinical outcomes or its ability to reflect slow, diffuse neurodegenerative damage in multiple sclerosis is less clear. However, early results from real-world cohorts and clinical trials using sNfL as a marker of treatment response in multiple sclerosis are encouraging. Importantly, clinical algorithms should now be developed that incorporate the routine use of sNfL to guide individualized clinical decision-making in people with multiple sclerosis, together with additional fluid biomarkers and clinical and MRI measures. Here, we propose specific clinical scenarios where implementing sNfL measures may be of utility, including, among others: initial diagnosis, first treatment choice, surveillance of subclinical disease activity and guidance of therapy selection.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".