LupusQoL-US Benchmarks for US Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".