Summary of Research: Blood Biomarker Dynamics in People with Relapsing Multiple Sclerosis Treated with Cladribine Tablets: Results of the 2-Year MAGNIFY-MS Study
Bibliographic record
Abstract
Cladribine tablets (CladT) for relapsing multiple sclerosis help reset the immune system with short treatment courses over 2 years. This analysis of MAGNIFY-MS contrasted clinical outcomes with changes in immune cells, proteins and genes over 2 years in 270 participants. Most immune cells decreased 3 months after starting CladT. Gradual recovery was seen in naïve, regulatory, and transitional B cells starting at month (M)3 and M6. Gene activity related to immune response changes was also reported. Fewer cells producing pro-inflammatory signals and more cells producing anti-inflammatory signals were detected by M24. Immunoglobulin levels mostly remained normal, and a marker of neuroaxonal damage (serum neurofilament light chain) was decreased. Significant reductions in lesion count occurred from M2 onwards. Annualised relapse rate was 0.11 (95% confidence interval: 0.09, 0.15). Over 90% of participants were free of 6-month confirmed disability progression, around 87% had no confirmed progression on 9-hole peg test and timed 25-foot walk. No significant correlations were seen between clinical parameters and lymphocyte dynamics. The safety profile was consistent with previous studies. These findings provide evidence of CladT rebalancing the immune system towards a more homeostatic and less pathogenic state.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".