Serum neurofilament light in MS: The first true blood-based biomarker?
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
A simple blood-derived biomarker is desirable in the routine management of multiple sclerosis (MS) patients and serum neurofilament light chain (sNfL) is the most promising candidate. Although its utility was first shown in cerebrospinal fluid (CSF), technological advancements have enabled reliable detection in serum and less frequently plasma, obviating the need for repeated lumbar punctures. In this review, after defining the knowledge gap in MS management that many hope sNfL could fill, we summarize salient studies demonstrating associations of sNfL levels with outcomes of interest. We group these outcomes into inflammatory activity, progression, treatment response, and prediction/prognosis. Where possible we focus on data from real-world perspective observational cohorts. While acknowledging the limitations of sNfL and highlighting key areas for ongoing work, we conclude with our opinion of the role for sNfL as an objective, convenient, and cost-effective adjunct to clinical assessment. Paving the way for other promising biomarkers both blood-derived and otherwise, sNfL is an incremental step toward precision medicine for MS patients.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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 it