Serum phosphorylated neurofilament-heavy chain levels in multiple sclerosis patients
Bibliographic record
Abstract
OBJECTIVES: We evaluated whether the measurement of serum phosphorylated neurofilament heavy chain (pNF-H) titre is likely to be a valid biomarker of axonal injury in multiple sclerosis (MS). METHODS: Serum pNF-H concentrations were measured by ELISA in cases with relapsing-remitting (RR)-MS (n=81), secondary progressive (SP) MS (n=13) and primary progressive (PP)-MS; n=6) MS; first demyelinating event (FDE; n=82); and unaffected controls (n=135). A subset of MS cases (n=45) were re-sampled on one or multiple occasions. The Multiple Sclerosis Severity Score (MSSS) and MRI measures were used to evaluate associations between serum pNF-H status, disease severity and cerebral lesion load and activity. RESULTS: We confirmed the presence of pNF-H peptides in serum by ELISA. We showed that a high serum pNF-H titre was detectable in 9% of RR-MS and FDE cases, and 38.5% of SP-MS cases. Patients with a high serum pNF-H titre had higher average MSSS scores and T2 lesion volumes than patients with a low serum pNF-H titre. Repeated sampling of a subset of MS cases showed that pNF-H levels can fluctuate over time, likely reflecting temporal dynamics of axonal injury in MS. CONCLUSIONS: A subset of FDE/MS cases was found to have a high serum pNF-H titre, and this was associated with changes in clinical outcome measures. We propose that routine measurement of serum pNF-H should be further investigated for monitoring axonal injury in MS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".