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Record W2083757671 · doi:10.1155/2014/651921

Co-Occurrence of Arthritis and Stroke amongst Middle-Aged and Older Adults in Canada

2014· article· en· W2083757671 on OpenAlexaffabout
Roman Matveev, Chris I. Ardern

Bibliographic record

VenueStroke Research and Treatment · 2014
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsYork University
Fundersnot available
KeywordsMedicineLogistic regressionStroke (engine)ArthritisAlgorithmPopulationCross-sectional studyInternal medicineGerontologyDemographyMathematicsPathology

Abstract

fetched live from OpenAlex

Arthritis is a chronic inflammatory condition commonly associated with mobility restriction and reduced activity. To date, the extent to which arthritis is an independent risk factor for stroke is unclear, and important, in light of an aging population. The purpose of this study was to (i) quantify the cross-sectional association between stroke and arthritis and (ii) to determine whether the relationship differed in physically active and inactivemiddle-aged and older adults. Data was derived from the 2010 Canadian Community Health Survey (N = 47 188; ≥30 y). Multivariable logistic regression was used to estimate the association between arthritis and stroke in models adjusted for age, physical activity (PA), and demographic factors. Overall, individuals with arthritis were 4 times more likely to report a history of stroke (OR = 3.8, 95% CI = 3.06-4.68), whereas those who were engaged in at least moderate PA (≥ 1.5 kcal/kg/day) were less than half as likely (0.45, 0.92-0.62). This effect was moderated by age, as younger (30-65 y: 3.27, 2.22-4.83) but not older adults (>65 y: 1.04, 0.8-1.35) with arthritis had elevated odds of stroke. Both physical inactivity and arthritis are associated with higher odds of stroke, effects of which are the strongest amongst 30-65 year olds.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.355
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

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

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 head0.025
GPT teacher head0.292
Teacher spread0.267 · 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.

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

Citations1
Published2014
Admission routes2
Has abstractyes

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