Predictive nature of IgM anti-α-glucose serum biomarker for relapse activity and EDSS progression in CIS patients: a BENEFIT study analysis
Bibliographic record
Abstract
BACKGROUND: Higher serum levels of at least one of a panel of four α-glucose IgM antibodies (gMS-Classifier1) in clinically isolated syndrome (CIS) patients are associated with imminent early relapse within 2 years. OBJECTIVE: The objective of this study was to determine the prognostic value of gMS-Classifier1 in a large study cohort of CIS patients. METHODS: The BEtaseron(®) in Newly Emerging multiple sclerosis For Initial Treatment (BENEFIT) 5-year study was designed to evaluate the impact of early versus delayed interferon-β-1b (IFNβ-1b; Betaseron(®)) treatment in patients with a first event suggestive of multiple sclerosis (MS). Patients (n = 258, 61% of total) with a minimum of 2 ml baseline serum were eligible for the biomarker study. gMS-Classifier1 antibodies' panel (anti-GAGA2, anti-GAGA3, anti-GAGA4 and anti-GAGA6) levels were measured blinded to clinical data. Subjects were classified as either 'positive' or 'negative' according to a classification rule. RESULTS: gMS-Classifier1 was not predictive for the time to clinically definite MS or time to MS according to the revised McDonald's criteria, but did significantly predict an increased risk for confirmed disability progression (log-rank test: p = 0.012). CONCLUSIONS: We could not confirm previous results that gMS-Classifier1 can predict early conversion to MS in CIS. However, raised titres of these antibodies may predict early disability progression in this patient population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".