Intrathecal interleukin-6 levels are associated with progressive disease and clinical severity in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: MS is characterized by persistent central nervous system (CNS) inflammation. Investigating the CNS-compartmentalized inflammation associated with progressive MS could uncover new biomarkers and therapeutic targets. Cerebrospinal fluid (CSF) interleukin-6 (IL-6) can be markedly elevated in neuroinflammatory conditions, such as neuromyelitis optica spectrum disorder and myelin oligodendrocyte glycoprotein antibody-associated disease. This study investigated the association between CSF IL-6 levels, progressive disease, and disease severity in MS. METHODS: Advanced technologies, including single-molecule arrays and microfluidics, were used to analyse CSF samples from individuals with MS at the time of diagnosis for IL-6. IL-6 levels were then correlated with clinical course, disease severity, and other known biomarkers associated with inflammation and disease severity. RESULTS: Elevated IL-6 levels in the CSF were found in individuals with progressive MS, and CSF IL-6 showed positive correlations with the Expanded Disability Status Scale, the Multiple Sclerosis Severity Score, and CSF glial fibrillary acidic protein levels. CONCLUSIONS: IL-6 in CSF indicates ongoing CNS inflammation and may contribute to the compartmentalized inflammation associated with disease progression and overall disease severity.
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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 | 0.002 |
| 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 | 0.001 |
| 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".