THUR 174 The magnify-ms study: mavenclad® tablets in active rms
Bibliographic record
Abstract
Authors Disclaimer: http://medpub-poster.merckgroup.com/ABN2018DISC_MAGNIFY.pdf Background Cladribine tablets (CT) improve clinical and MRI outcomes in patients with active RMS, with significant differences versus placebo after 24 weeks. Objective Describe the design of a study to assess the onset of CT’s clinical and MRI effects in patients with active RMS. Methods MAGNIFY-MS is a 2 year prospective Phase IV trial (including approximately 100 centres in Europe). Eligible patients will receive two years treatment with CT 3.5 mg/kg cumulative dose. Frequent MRI assessments (including lesion count, lesion volume, brain volume and MTR) will be performed at screening, baseline and 1, 2, 3, 6, 12, 15, 18 and 24 months. Various T- and B-cell subtype counts and functional profiling (eg cytokine production) will be assessed. Clinical outcomes will include changes in cognition (SDMT), disability (EDSS/KFS, 9HPT, T25FW), relapses, NEDA, NEDAP and safety at timepoints up to 24 months. Results Aim recruit 300 patients. Primary endpoint: change in the count of combined unique active lesions at end of 6 months versus baseline. Final outcomes expected in 2021. Conclusions MAGNIFY-MS will provide important information on the effects of CT, including early MRI changes, insights into effects on a range of disability and cognition markers, and detailed characterization of immune cell reconstitution. Disclosure statement This study was sponsored by EMD Serono, Inc., a business of Merck KGaA, Darmstadt, Germany (in the USA), and Merck Serono SA – Geneva, an affiliate of Merck KGaA Darmstadt, Germany (ROW).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".