MétaCan
Menu
Back to cohort
Record W2049465834 · doi:10.1002/art.23344

Clinical correlates of quality of life in systemic sclerosis measured with the World Health Organization Disability Assessment Schedule II

2008· article· en· W2049465834 on OpenAlexaffabout
Marie Hudson, Brett D. Thombs, Russell Steele, R. P. Watterson, Suzanne Taillefer, Murray Baron

Bibliographic record

VenueArthritis Care & Research · 2008
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Physical therapyDepression (economics)Scleroderma (fungus)DiseaseMultivariate analysisBayesian multivariate linear regressionInternal medicineLinear regressionPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify the clinical characteristics of systemic sclerosis (SSc) that best correlate with the health-related quality of life (HRQOL) of patients with SSc, using the World Health Organization Disability Assessment Schedule II (WHODAS II) as the measure of HRQOL. METHODS: A cross-sectional, multicenter study of 337 patients from the Canadian Scleroderma Research Group Registry was conducted. Patients were assessed through detailed clinical histories, medical examination, and the WHODAS II. Hierarchical multiple linear regression was used to assess the relationship between selected clinical variables and HRQOL. RESULTS: The mean WHODAS II score was 23.7 (range 0-100), with the greatest impairments seen in the subscales measuring life activities, mobility, and participation in society. In multivariate analysis, significant predictors of the WHODAS II were skin scores, shortness of breath, number of gastrointestinal problems, fatigue, pain, and depression. The final model explained 61% of the variance in the WHODAS II scores. CONCLUSION: The clinical characteristics identified in this study as significant correlates of HRQOL in SSc should each be targets of intervention in order to improve the HRQOL of patients with this disease.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.020
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.151
GPT teacher head0.422
Teacher spread0.271 · 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

Citations64
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueArthritis Care & ResearchSame topicSystemic Sclerosis and Related DiseasesFrench-language works237,207