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Record W3082887946 · doi:10.1152/jn.00165.2020

Multiple sclerosis-related fatigue: the role of impaired corticospinal responses and heightened exercise fatigability

2020· article· en· W3082887946 on OpenAlexaff
Kyla Coates, Saied Jalal Aboodarda, Renata Lopes Krüger, Tristan Martin, Luanne M. Metz, Scott E. Jarvis, Guillaume Y. Millet

Bibliographic record

VenueJournal of Neurophysiology · 2020
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMultiple sclerosisPhysical medicine and rehabilitationMuscle fatigueMedicinePsychologyPhysical therapyNeuroscienceElectromyographyPsychiatry

Abstract

fetched live from OpenAlex

The etiology of fatigability from whole body exercise was examined for the first time to accurately elucidate the relationship between fatigue and fatigability in multiple sclerosis (MS). Compromised corticospinal responsiveness predicted fatigue severity, providing a novel, objective indicator of fatigue in MS. Although the impaired corticomotor transmission did not aggravate muscle activation in this group of people with multiple sclerosis (PwMS) of lower disability, heightened muscle fatigability was seen to contribute to perceptions of fatigue in PwMS.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.303
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations30
Published2020
Admission routes1
Has abstractyes

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