Serum neurofilament‐light and glial fibrillary acidic protein levels in hydroxychloroquine‐treated primary progressive multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: In a recent trial, hydroxychloroquine (HCQ) treatment reduced the expected rate of disability worsening at 18 months in primary progressive multiple sclerosis (PPMS). Neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are emerging biomarkers in multiple sclerosis. METHODS: We measured NfL and GFAP levels in serum samples from 39 patients with inactive PPMS included in a phase II clinical trial of HCQ treatment in PPMS at multiple time points over 18 months, and investigated the association of these biomarkers with clinical disability at screening and during follow-up. Screening and 12-month retinal nerve fiber layer (RNFL) thickness was also recorded and analyzed. RESULTS: NfL and GFAP levels increased over time, but only significantly from screening to month 6. NfL and GFAP levels did not significantly increase from month 6 up to month 18. At screening, NfL and GFAP levels did not correlate with the Expanded Disability Status Scale (EDSS), and GFAP but not NfL modestly correlated with Timed 25-Foot Walk test (T25FW). Screening NfL and GFAP levels did not predict disability worsening (≥20% worsening on the T25FW) at month 18. RNFL thickness decreased significantly from screening to month 12 and independently predicted disability worsening. CONCLUSIONS: In this cohort of people with inactive PPMS, HCQ treatment attenuated the increase of NfL and GFAP after 6 months of treatment and up to 18 months of follow-up, suggesting a treatment effect of HCQ over these biomarkers. RNFL thickness, a marker of neuroaxonal atrophy, was associated with disability worsening, and should be explored further as a prognostic marker in this population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".