The role of serum neurofilament light (sNfL) as a biomarker in multiple sclerosis: insights from a systematic review
Bibliographic record
Abstract
OBJECTIVE: This systematic literature review (SLR) was conducted to explore the role of serum neurofilament light chain (sNfL) as a biomarker in multiple sclerosis (MS) disease management. METHODS: -In-Process, and all Evidence-Based Medicine [EBM] Reviews databases) to retrieve studies reporting the association between sNfL and disease activity in patients with MS. Additional evidence was also identified through hand searching of key conference proceedings and gray literature. RESULTS: Following review of 1831 records, 75 studies from 180 publications were included in the review. The studies included in the SLR consistently demonstrated an association between higher sNfL levels and an increased risk of future relapses within 2 years and MS disease progression. Higher levels of sNfL were also linked to an increased likelihood of experiencing gadolinium-enhancing T1 and T2 lesions. Patients with lower sNfL levels had a higher likelihood of achieving no evidence of disease activity status. Furthermore, an inverse correlation was observed between sNfL levels and cognitive impairment as assessed via the Symbol Digit Modalities Test performance and Timed 25-Foot Walk scores. CONCLUSION: This SLR demonstrates the significance of sNfL as a sensitive biomarker for monitoring MS progression. Convenient and reliable sNfL measurement could benefit routine clinical practice, providing clinicians with a simple and effective tool to monitor disease and treatment response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".