Temporal profile of serum neurofilament light in multiple sclerosis: Implications for patient monitoring
Bibliographic record
Abstract
Objective: To understand how longitudinal serum neurofilament light chain (sNfL) patterns can inform its use as a prognostic biomarker in multiple sclerosis (MS) and evaluate whether sNfL reflects MS disease activity and disease-modifying therapy usage. Methods: This was a post hoc analysis of longitudinal data and samples from the ADVANCE trial (NCT00906399) of patients with relapsing–remitting MS (RRMS). sNfL was measured every 3 months for 2 years, then every 6 months for 4 years. Regression models explored how sNfL data predicted 4-year values of brain volume, expanded disability status scale score, and T2 lesions. sNfL levels were assessed in those receiving placebo, peginterferon beta-1a, and those with disease activity. Results: Baseline sNfL was a predictor of 4-year brain atrophy and development of new T2 lesions. Clinical ( p = 0.02) and magnetic resonance imaging (MRI) ( p < 0.01) outcomes improved in those receiving peginterferon beta-1a whose sNfL decreased to <16 pg/mL after 12 months versus those whose sNfL remained ⩾16 pg/mL. Mean sNfL levels decreased in peginterferon beta-1a-treated patients and increased in placebo-treated patients (–9.5% vs. 6.8%; p < 0.01). sNfL was higher and more variable in patients with evidence of active MS. Conclusion: These data support sNfL as a prognostic and disease-monitoring biomarker for RRMS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".