3003 Prognostic value of on-treatment serum neurofilament light chain for neT2 lesions relapsing MS patients: pooled analysis of ASCLEPIOS I/II
Bibliographic record
Abstract
Objectives To evaluate the prognostic value of 3- and 12-month on-treatment serum neurofilament light chain (sNfL) levels for future disease activity in with relapsing multiple sclerosis[pwRMS]Methods A baseline sNfL cut-off was predefined by the median sNfL value across the ofatumumab ASCLEPIOS I/II Phase 3 clinical trials and participants were stratified into high (≥baseline median [≥9.3 pg/mL]) and low (<median) sNfL groups at Month (M) 3 and M12, irrespective of treatment received. The prognostic value of high versus low sNfL at M3 and M12 was analyzed for the annualized rate of new/enlarging T2 (neT2) lesions. The number of neT2 lesions on the last available scan relative to the M12 scan was analyzed in a negative binomial model with time (in years) between the two scans as offset, adjusting for sNfL category at the respective month.Results Of the 1,882 participants randomized in ASCLEPIOS I/II, 1,393 and 1,384 participants had neT2 and sNfL data at M3 andM12, respectively. Participants with high versus low sNfL at M3 had a ~2.2-fold higher mean annualized rate of neT2 lesions (3.67versus 1.69, rate ratio [RR]: 2.17; p<0.001). Similarly, participants with high versus low sNfL at M12 had a ~3.6-fold higher mean annualized rate of neT2 lesions (4.90 versus 1.37, RR: 3.57; p<0.001).Conclusions On-treatment sNfL levels at 3 and 12 months continue to be prognostic for future lesion formation and support the use of sNfL as a prognostic biomarker for MS disease activity in pwRMS on disease-modifying therapy.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".