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Record W4387950254 · doi:10.1177/13524585231204460

The MoXFo Initiative: Using consensus methodology to move forward towards internationally shared vocabulary in multiple sclerosis exercise research

2023· article· en· W4387950254 on OpenAlexaff
Maedeh Mansoubi, Yvonne C. Learmonth, Nancy E. Mayo, Johnny Collet, Helen Dawes

Bibliographic record

VenueMultiple Sclerosis Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill University
FundersDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsMultiple sclerosisVocabularyPhysical medicine and rehabilitationConsensus conferenceMEDLINEPsychologyMedicinePhysical therapyPolitical scienceLinguisticsInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Multiple sclerosis (MS) exercise terminology lacks consistency across disciplines, hindering research synthesis. OBJECTIVE: The 'Moving exercise research in MS forward initiative' (MoXFo) aims to establish agreed definitions for key MS exercise terms. METHODS: The Lexicon development methodology was employed. A three-step process identified key exercise terminology for people with multiple sclerosis (pwMS): (1) consensus and systematic review, (2) Delphi round 1 and consideration of existing definitions and (3) Delphi round 2 for consensus among MoXFo steering group and exercise experts. Final definitions and style harmonisation were agreed upon. RESULTS: The two-stage Delphi process resulted in the selection and scoring of 30 terminology definitions. The agreement was 100% for resistance exercise, balance and physical activity. Most terms had agreement >75%, but 'posture' (60%) and 'exercise' (65%) had a lower agreement. CONCLUSION: This study identified key terms and obtained agreement on definitions for 30 terms. The variability in agreement for some terms supports the need for clearly referencing or defining terminology within publications to enable clear communication across disciplines and to support precise synthesis and accurate interpretation of research.

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.

How this classification was reachedexpand

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)0.014
metaresearch head score (Gemma)0.036
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.003
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 head0.589
GPT teacher head0.451
Teacher spread0.138 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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