Routine CSF parameters as predictors of disease course in multiple sclerosis: an MSBase cohort study
Bibliographic record
Abstract
Background It remains unclear whether routine cerebrospinal fluid (CSF) parameters can serve as predictors of multiple sclerosis (MS) disease course. Methods This large-scale cohort study included persons with MS with CSF data documented in the MSBase registry. CSF parameters to predict time to reach confirmed Expanded Disability Status Scale (EDSS) scores 4, 6 and 7 and annualised relapse rate in the first 2 years after diagnosis (ARR2) were assessed using (cox) regression analysis. Results In total, 11 245 participants were included of which 93.7% (n=10 533) were persons with relapsing-remitting MS (RRMS). In RRMS, the presence of CSF oligoclonal bands (OCBs) was associated with shorter time to disability milestones EDSS 4 (adjusted HR=1.272 (95% CI, 1.089 to 1.485), p=0.002), EDSS 6 (HR=1.314 (95% CI, 1.062 to 1.626), p=0.012) and EDSS 7 (HR=1.686 (95% CI, 1.111 to 2.558), p=0.014). On the other hand, the presence of CSF pleocytosis (≥5 cells/µL) increased time to moderate disability (EDSS 4) in RRMS (HR=0.774 (95% CI, 0.632 to 0.948), p=0.013). None of the CSF variables were associated with time to disability milestones in persons with primary progressive MS (PPMS). The presence of CSF pleocytosis increased ARR2 in RRMS (adjusted R2=0.036, p=0.015). Conclusions In RRMS, the presence of CSF OCBs predicts shorter time to disability milestones, whereas CSF pleocytosis could be protective. This could however not be found in PPMS. CSF pleocytosis is associated with short-term inflammatory disease activity in RRMS. CSF analysis provides prognostic information which could aid in clinical and therapeutic decision-making.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".