Moving exercise research in multiple sclerosis forward (the MoXFo initiative): Developing consensus statements for research
Bibliographic record
Abstract
Exercise as a subset of physical activity is a cornerstone in the management of multiple sclerosis (MS) based on its pleotropic effects. There is an exponential increase in the quantity of research on exercise in MS, yet a number of barriers associated with study content and quality hamper rapid progress in the field. To address these barriers and accelerate discovery, a new international partnership of MS-related experts in exercise has emerged with the goal of advancing the research agenda. As a first step, the expert panel met in May 2018 and identified the most urgent areas for moving the field forward, and discussed the framework for such a process. This led to identification of five themes, namely "Definitions and terminology," "Study methodology," "Reporting and outcomes," "Adherence to exercise," and "Mechanisms of action." Based on the identified themes, five expert groups have been formed, that will further (a) outline the challenges per theme and (b) provide recommendations for moving forward. We aim to involve and collaborate with people with MS/MS organizations (e.g. Multiple Sclerosis International Federation (MSIF) and European Multiple Sclerosis Platform (EMSP)) in all of these five themes. The generation of this thematic framework with multi-expert perspectives can bolster the quality and scope of exercise studies in MS that may ultimately improve the daily lives of people with MS.
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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 | 0.635 | 0.457 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.009 | 0.029 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".