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Record W2792025795 · doi:10.1177/2055217318754368

National estimates of self-reported sitting time in adults with multiple sclerosis

2018· article· en· W2792025795 on OpenAlexaff
JE Sasaki, RW Motl, Gary Cutter, Ruth Ann Marrie, Tuula Tyry, Amber Salter

Bibliographic record

VenueMultiple Sclerosis Journal - Experimental Translational and Clinical · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Manitoba
FundersNational Multiple Sclerosis Society
KeywordsSittingMedicineUnderweightPhysical therapyDemographyPopulationMultiple sclerosisPediatricsInternal medicineBody mass indexPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Limited data exist on the prevalence and distribution of sedentary behavior (SB) in multiple sclerosis (MS). OBJECTIVE: The objective of this paper is to describe sitting time as a metric of SB in a large national sample of people with MS. METHODS: A total of 8004 individuals from the North American Research Committee on MS (NARCOMS) Registry completed the sitting time question from the International Physical Activity Questionnaire in spring 2015. We present descriptive data on sitting time for the total sample and across sociodemographic, clinical, and behavioral characteristics. RESULTS: The final sample included 6483 individuals. Of these, 36.7% were classified with mild disability, 24.7% with moderate disability, and 38.6% with severe disability. Median sitting time for the total sample was 480 min/day (P25 = 310 min/day, P75 = 720 min/day). Sitting time was highest for individuals with MS who were male (540 min/day), not married (540 min/day), had a disease duration >30 years (540 min/day), were underweight (540.5 min/day), had an annual income of < $15,000 (585 min/day), presented with a progressive form of MS (600 min/day), were classified as insufficiently active (600 min/day), or presented with severe disability (661 min/day). CONCLUSION: Sitting time is twice as high in individuals with MS compared to the general population (240 min/day).

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.138
GPT teacher head0.373
Teacher spread0.235 · 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

Citations34
Published2018
Admission routes1
Has abstractyes

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