Education, Zip Code-based Annualized Household Income, and Health Outcomes in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To determine the association of socioeconomic status [SES; education and zip code-based annual household income (Z-AHI)] and ethnicity with health-related quality of life (HRQOL) among patients with systemic lupus erythematosus (SLE). METHODS: Data on 211 subjects from a cross-sectional study (LupusPRO) using the Medical Outcomes Study Short Form-36 questionnaire to evaluate physical health scores (PCS) and mental health scores were used to obtain education and zip code. The 2000 US Census was used to obtain each zip code's median annual household income. RESULTS: Education and Z-AHI correlated with PCS (education standardized beta = 0.17, 95% CI 0.47, 3.65, p = 0.01, r(2) = 0.03; Z-AHI standardized beta = 0.15, 95% CI 0.57, 8.30, p = 0.02, r(2) = 0.02) on regression analysis. Z-AHI was linked to PCS, independent of education. Ethnicity was associated with PCS through disease activity and SES. CONCLUSION: SES is associated with HRQOL in SLE. Z-AHI and education are equally predictive surrogates of SES; however, Z-AHI, independent of education, was predictive of HRQOL. Z-AHI has less subject bias and is easily obtainable, therefore its use for future HRQOL studies is suggested.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".