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Record W2482237976 · doi:10.17925/usn.2015.11.01.19

Physical Activity in Pediatric Multiple Sclerosis—Can Lifestyle Factors Affect Disease Outcomes?

2015· article· en· W2482237976 on OpenAlexafffund
E. Ann Yeh, Robert W. Motl

Bibliographic record

VenuetouchREVIEWS in Neurology · 2015
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick ChildrenMultiple Sclerosis SocietyMultiple Sclerosis Society of CanadaCanadian Institutes of Health ResearchMultiple Sclerosis Scientific Research FoundationPublic Health AgencyDairy Farmers of OntarioPublic Health Agency of CanadaEMD SeronoNational Multiple Sclerosis Society
KeywordsMedicineAffect (linguistics)Multiple sclerosisDepression (economics)DiseasePsychological interventionNeuroinflammationPopulationCognitionPhysical activityPhysical therapyPhysical medicine and rehabilitationClinical psychologyGerontologyPsychiatryPsychologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Currently, little to no information is available about interventions that can ameliorate symptoms such as depression and fatigue in children and adolescents with multiple sclerosis (MS), nor is there clear information on modifiable factors that can provide neuroprotection in this population. However, physical activity (PA) may have significant effects on disease activity, future disability, cognition, and symptoms of depression and fatigue in pediatric MS. The extent of this effect is unknown. In this paper, after providing an overview of definitions of and outcomes in pediatric MS, we provide a review of existing literature relating PA to outcomes in MS, and then turn to a review of the complex relationship between PA, neuroinflammation, and outcomes in the pediatric population.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.158
GPT teacher head0.369
Teacher spread0.212 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2015
Admission routes2
Has abstractyes

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