Association of Poverty Income Ratio with Physical Functioning in a Cohort of Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To examine the association of income relative to the poverty threshold [poverty income ratio (PIR)] with self-reported physical functioning (PF) in a cohort of patients with systemic lupus erythematosus. METHODS: We used cross-sectional data on 744 participants from Georgians Organized Against Lupus (GOAL), and secondary analyses used data on 56 participants from a nested pilot study. Primary analyses used multivariable linear regression to estimate the association between PIR (categorized as < 1.00, 1.00-1.99, 2.00-3.99, and ≥ 4.00; lower PIR indicate higher poverty) and PF (scaled subscore from the Medical Outcomes Study Short Form-12 survey; range 0-100, higher scores indicate better functioning). Secondary analyses summarized complementary measures of PF as means or percentages by PIR (categorized as < 1.00, 1.00-1.99, and ≥ 2.00). RESULTS: The mean age of participants was 48.0 years; 6.7% were male; 80.9% were black; and 37.5%, 21.0%, 29.6%, and 12.0% had PIR of < 1.00, 1.00-1.99, 2.00-3.99, and ≥ 4.00, respectively. The overall mean PF score was 45.8 (36.2, 40.7, 55.5, and 61.2 for PIR of < 1.00, 1.00-1.99, 2.00-3.99, and ≥ 4.00). With adjustment, higher PIR remained associated with higher PF scores [2.00-3.99 vs 1.00-1.99: β = 10.9 (95% CI 3.3-18.6); ≥ 4.00 vs 1.00-1.99: β = 16.2 (95% CI 6.4-26.0)]. In secondary analyses, higher PIR was also associated with higher scores for objective physical performance. CONCLUSION: Our results show that higher income relative to the poverty threshold is associated with better PF across multiple domains, warranting further research into multicomponent functional assessments to develop individual treatment plans and potentially improve socioeconomic disparities in outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".