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Record W3006847841 · doi:10.1177/1352458520910360

Moving exercise research in multiple sclerosis forward (the MoXFo initiative): Developing consensus statements for research

2020· article· en· W3006847841 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMultiple Sclerosis Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMemorial University of NewfoundlandUniversity of Ottawa
Fundersnot available
KeywordsGeneral partnershipScope (computer science)TerminologyMultiple sclerosisQuality (philosophy)Thematic analysisProcess (computing)MedicinePublic relationsPsychologyQualitative researchMedical educationPolitical scienceComputer scienceSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.647
GPT teacher head0.467
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it