P.043 Long term MS clinical outcomes predicted by baseline serum neurofilament light levels
Bibliographic record
Abstract
Background: Prognostic biomarkers are badly needed to direct MS treatment intensity early in the condition Levels of serum neurofilament light chains (sNfL) result from the destruction of central nervous system axons in MS and correlate with the aggressiveness of the disease. Methods: In this prospective cohort study, we identified patients with serum collected within 5 years of first MS symptom onset with more than 15 years of clinical follow-up. Levels of sNfL were quantified in patients and matched controls using digital immunoassay. Results: Sixty-seven patients had a median follow-up period of 17.4 years (range:15.1-26.1). Median serum NfL levels in baseline samples of MS patients was 10.1 pg/ml, 38.5% higher than median levels in 37 controls (7.26pg/ml, p=0.004). Baseline NfL level was most helpful as a predictive marker to rule out progression; patients with levels less 7.62pg/ml were 4.3 times less likely to develop an EDSS score of 34 (p=0.001) and 7.1 times less likely to develop progressive MS (p=0.054). Patients with the highest NfL levels (3rd-tertile, >13.2 pg/ml) progressed most rapidly with an EDSS annual rate of 0.16 (p=0.004), remaining significant after adjustment for sex, age, and disease-modifying treatment (p=0.022). Conclusions: This study demonstrates that baseline sNfL is associated with long term disease progression.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".