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Record W2009653074 · doi:10.3899/jrheum.091443

LupusQoL-US Benchmarks for US Patients with Systemic Lupus Erythematosus

2010· article· en· W2009653074 on OpenAlexvenueno aff
Meenakshi Jolly, A. Simon Pickard, Rachel A. Mikolaitis, Roger A. Rodby, Winston Sequeira, Joel A. Block

Bibliographic record

VenueThe Journal of Rheumatology · 2010
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCohortEthnic groupMarital statusQuality of life (healthcare)DiseaseSystemic lupus erythematosusDemographyDescriptive statisticsCohort studyGerontologyInternal medicinePhysical therapyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: The LupusQoL-US instrument was recently validated in the US. We studied the benchmarks for a US patient cohort with systemic lupus erythematosus (SLE) and relevant demographic and disease correlates. METHODS: LupusQoL-US was administered to 185 patients with SLE. Demographic data (age, sex, ethnicity, marital status) and disease features (duration, disease activity and damage) were assessed simultaneously. Descriptive statistics were obtained. LupusQoL-US domain scores were calculated, and compared by sex, ethnicity, and marital status using nonparametric tests. Correlation between LupusQoL-US domains and age, disease duration, disease activity, and disease damage were obtained. RESULTS: Mean age of patients was 42.2 +/- 14.5 years; 94% of subjects were women. African American patients comprised 60% of the study cohort. The most affected domains were Fatigue and Physical Health. The least affected was Intimate Relationships. Age correlated with Physical Health, Pain, and Body Image (r = 0.15-0.18). Differences were observed based on sex and marital status, but not by ethnicity; there the LupusQoL-US correlated inversely with disease activity (r = -0.001 to -0.36) and damage (r = -0.003 to -0.40). CONCLUSION: All domains of the LupusQoL-US based health related quality of life (HRQOL) were affected adversely. HRQOL varied by age, sex, and marital status in our SLE cohort.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.262
Teacher spread0.252 · 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

Citations47
Published2010
Admission routes1
Has abstractyes

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