Multiple Sclerosis International Federation guideline methodology for off-label treatments for multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: A total of 2.8 million people are living with multiple sclerosis and due to disparities in access to medicines, the ability to treat this condition varies widely. Off-label disease-modifying therapies are sometimes more available or affordable in different health systems. Appropriate methodology is integral in creating high-quality and trustworthy guidelines. In this article, we outline Multiple Sclerosis International Federation's (MSIF) approach to creating guidelines for off-label treatments for multiple sclerosis. METHODS: We use the Guidelines International Network (GIN)-McMaster Guideline Development Checklist and the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) Evidence-to-Decision (EtD) framework. We developed detailed health descriptors for health outcomes and the panel drafted PICO (Population, Intervention, Comparator, Outcome) questions and prioritised outcomes. We collaborate with independent organisations, which systematically review and collate the information. We are actively engaging stakeholders and consulting with relevant organisations, boards, working groups and individuals. RESULTS: The draft guideline recommendations will be published for open comment and stakeholders will be encouraged to endorse and disseminate the guidelines. Our methodology ensures integrity and transparency in the criteria, evidence and judgement used to make recommendations. CONCLUSIONS: This approach will facilitate transparent creation of high-quality and trustworthy guidelines, and allow the global guidelines to be adopted or adapted into national settings.
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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 | 0.157 | 0.313 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".