Plasma neurofilament light chain in children with relapsing MS receiving teriflunomide or placebo: A post hoc analysis of the randomized TERIKIDS trial
Bibliographic record
Abstract
Background: The phase 3 TERIKIDS study demonstrated efficacy and manageable safety for teriflunomide versus placebo in children with relapsing multiple sclerosis (RMS). Objective: Evaluate plasma neurofilament light chain (pNfL) concentrations in TERIKIDS. Methods: Patients received placebo or teriflunomide (14 mg adult equivalent) for up to 96 weeks in the double-blind (DB) period. In the open-label extension (OLE), all patients received teriflunomide until up to 192 weeks after randomization. pNfL was measured using single-molecule array assay (Simoa ® NF-light ™ ). Results: Baseline mean age was 14.5 years; 69.4% were female. Baseline geometric least square mean pNfL levels were similar for teriflunomide ( n = 78) and placebo ( n = 33) patients (19.83 vs 18.30 pg/mL). Over the combined DB and OLE periods, pNfL values were lower for teriflunomide versus placebo (analysis of variance p < 0.01; Week 192: 10.61 vs 17.32 pg/mL). Observed between-group pNfL differences were attenuated upon adjustment for gadolinium (Gd)-enhancing or new/enlarged T2 lesion counts at DB Week 24. Higher baseline pNfL levels were associated with shorter time since first MS symptom onset, higher baseline Gd-enhancing lesion counts and T2 lesion volume, and increased hazard of high magnetic resonance imaging activity or clinical relapse during the DB period. Conclusion: Teriflunomide treatment was associated with significantly reduced pNfL levels in children with RMS. ClinicalTrials.gov identifier: NCT02201108.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".