Neurofilament light as a prognostic marker in multiple sclerosis
Bibliographic record
Abstract
Relapsing-remitting multiple sclerosis has a variable prognosis and lacks a reliable laboratory prognostic marker. Our aim in this study was to investigate the association between neurofilament light levels in cerebrospinal fluid in early multiple sclerosis and disease severity at long-term follow-up. Neurofilament light levels in cerebrospinal fluid collected at diagnostic lumbar puncture were measured in 99 multiple sclerosis cases. Clinical data were obtained from 95 out of those at follow-up visits made 14 years (range 8-20 years) after disease onset. Significant correlations between neurofilament light levels and the multiple sclerosis severity score were found for all cases (r = 0.30, p = 0.005), for relapsing-remitting multiple sclerosis cases (r = 0.47, p < 0.001) and for cases with a recent relapse (r = 0.60, p < 0.001). In the multivariate logistic regression analysis, neurofilament light levels >386 ng/L (median value of cases with detectable levels) increased the risk for severe multiple sclerosis fivefold (odds ratio 5.2, 95% confidence interval 1.8-15). Kaplan-Meier analysis showed that conversion to secondary-progressive multiple sclerosis was more likely in cases with neurofilament light levels >386 ng/L than in those with neurofilament light levels <60 ng/L (p = 0.01) or 60-386 ng/L (p = 0.03). We conclude that elevated levels of neurofilament light in cerebrospinal fluid collected at diagnostic lumbar puncture were associated with unfavourable prognosis. These data suggest that the neurofilament light level could be used as a prognostic marker in early relapsing-remitting multiple sclerosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".