The MoxFo initiative – outcomes: Outcome measures in studies of exercise training in multiple sclerosis; scoping review of reviews and classification according to the ICF framework
Bibliographic record
Abstract
BACKGROUND: The number of published studies of exercise training in multiple sclerosis (MS) has grown exponentially with increasing numbers of outcomes capturing exercise effects. This has complicated the selection of relevant indicators and interpretation of intervention effects. OBJECTIVES: The Outcomes subgroup of the MoXFo initiative aimed to (1) identify outcome measures and biomarkers in studies of exercise training in MS; (2) systematically map retrieved outcomes to International Classification of Functioning, Disability and Health (ICF) categories; (3) identify gaps where relevant ICF categories have been omitted. METHODS: Electronic databases and registers were searched from 2010 to July 2020 to identify systematic reviews or meta-analyses of controlled trials of exercise training on any outcome in MS. Retrieved outcomes/biomarkers were mapped to the corresponding ICF category. RESULTS: Eighty-one review articles reporting 235 different outcomes were included. The outcomes corresponded to 15 chapters and 45 categories within the ICF. Outcomes mapped primarily to body function (30 categories) and activities and participation (9 categories) components. Few outcomes mapped to body structures (2 categories) or environmental factors (1 category). CONCLUSION: This sets the stage to develop a resource for researchers/clinicians that will aid in the selection of appropriate outcomes/biomarkers when examining exercise effects in 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 | 0.085 | 0.216 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.015 |
| Bibliometrics | 0.090 | 0.067 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".