Clinical validation of a novel in vitro diagnostic neurofilament light chain assay for the prognostication of disease activity in people with relapsing multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Neurofilament light chain (NfL) is a promising marker for predicting disease activity in relapsing multiple sclerosis (RMS). To date, however, there has been no commercially available NfL assay validated in MS and intended for routine clinical use. OBJECTIVE: IM NfL assay. METHODS: The optimal NfL threshold for this assay/use case was identified and independently validated using ASCLEPIOS I and II data, respectively. The primary endpoint (annualized number of new/enlarging T2 (neT2) lesions) was analyzed using negative binomial models. Threshold optimization used maximum likelihood methodology. Generalizability analyses used data from ASCLEPIOS II, FREEDOMS, and TRANSFORMS. RESULTS: NfL concentration of 12.9 pg/mL was validated as the optimal cutoff for prognosticating disease activity as measured by neT2 lesion over 2 years. This threshold prognosticated individual patient risk for persistent disease activity (>3 neT2 lesions/year over 2 years) and showed prognostic value across relevant subgroups and clinical scenarios. Findings for relapses were similar. CONCLUSION: IM NfL assay is now validated for prognostic use in RMS patients.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".