The combination of CSF neurofilament light chain and glial fibrillary acidic protein improves the prediction of long-term confirmed disability worsening in multiple sclerosis
Bibliographic record
Abstract
Our objective was to evaluate the individual and combined prognostic attributes of baseline serum and CSF measurements of Neurofilament light chain (sNfL, cNfL) and glial fibrillary acidic protein (sGFAP, cGFAP) on long term clinical outcomes in MS. In this retrospective single center study, patients with serum and CSF stored at first MS presentation and > 15-years of follow-up were analyzed. NfL and GFAP were quantified from cryopreserved samples using a digital immunoassay and analyzed as predictors of confirmed disability worsening (CDW). Sixty patients (70% female) underwent baseline tandem CSF and serum sampling and were followed for a mean of 17.8 years (SD 2.5). 32 developed CDW. By logistic regression, while sNfL cNfL and cGFAP showed prognostic merit (AUC 0.72, 0.70, 0.62 respectively), sGFAP did not (AUC 0.5). The combination of cNfL and cGFAP improved CDW prediction compared to either measure considered in isolation (AUC 0.72). The optimal predictive cut-off for CDW (Youden's index) for cNfL was 596 pg/mL and for cGFAP was 8160 pg/mL. Kaplan-Meier analysis of the cutoff-defined 'high-high' and 'low-low' combined cNfL and cGFAP groupings improved prediction of CDW compared to either marker individually (Hazard ratio 4.5 (95% CI 2.7-18.3), Logrank P < 0.0001). Cox Proportional Hazards regression demonstrated that high baseline cNfL and cGFAP were independently prognostic of subsequent CDW after adjusting for baseline age, sex, EDSS score and subsequent treatment exposure. Each unit increase in Ln(cNfL) and Ln(cGFAP) was respectively associated with an additional hazard of 2.36 (95% CI 1.12-5.52) and 2.26 (95% CI 1.03-5.21). CSF NfL and GFAP are independently prognostic of long-term clinical worsening in MS, and may represent a complementary pairing.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".