Blood biomarkers for predicting disability worsening in progressive multiple sclerosis: a multinational, individual participant-level analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Biologically informative markers like glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) may help predict confirmed disability worsening (CDW) in multiple sclerosis (MS). However, data on the prognostic value of their blood concentrations in progressive MS (PMS) are limited, and there are substantial discrepancies in the published literature. This international collaboration uses individual participant data to define the prognostic value of serum GFAP and NfL in people with PMS (pwPMS). METHODS: Data were collected from BioMS-eu network centres and collaborating cohorts. pwPMS with primary progressive MS (PPMS) or secondary progressive MS (SPMS) with at least one GFAP value and at least three follow-up expanded disability status scale (EDSS) scores were included. The prognostic value of serum GFAP and NfL age- and sex-adjusted Z-scores for future CDW was evaluated using Cox regression models, accounting for sex, age, baseline disease duration and EDSS, and dominant treatment during follow-up. RESULTS: 1058 participants and 7530 encounters were included (median age 53 years (IQR: 44 to 59), 57% female, follow-up 4.6 years (2.9 to 8.4)) with median baseline GFAP of 0.74 (-0.10 to 1.55) and NfL of 0.64 (-0.36 to 1.51). 723 CDW events were recorded. Each GFAP Z-score increase was associated with ~10% higher CDW risk (adjusted HR (aHR) 1.107 (1.001 to 1.225), p=0.049). Results were mainly driven by SPMS participants (n=613, aHR 1.242 (1.073 to 1.438), p=0.004). Higher NfL Z-scores predicted CDW only in PPMS participants (1.236 (1.092 to 1.399), p=0.001). CONCLUSIONS: GFAP was a prognostic indicator for future CDW in pwPMS, especially in pwSPMS. On the other hand, NfL was predictive of CDW only in pwPPMS.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".