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 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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".