Influence of Education on Disease Activity and Damage in Systemic Lupus Erythematosus: Data From the 1000 Canadian Faces of Lupus
Bibliographic record
Abstract
OBJECTIVE: To determine whether socioeconomic status assessed by education is associated with disease activity and the risk of organ damage in systemic lupus erythematosus (SLE). METHODS: Data from the 1000 Canadian Faces of Lupus, a multicenter database of adult SLE patients, was used to compare education as either low (did not complete high school) or high (completed high school or further) for disease activity and damage. Education was also studied as a continuous variable. The relationships between education and SLE outcomes (any organ damage defined as a Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index [SDI] score ≥1, serious organ damage [SDI score ≥3], and end-stage renal disease) were evaluated using logistic regression analyses adjusted for age, sex, race/ethnicity, and disease duration. RESULTS: A total of 562 SLE patients met inclusion criteria (mean age 47 years, 91% female, and mean disease duration of 10 years); 81% had high education. The low education group was twice as likely to be work disabled (30%; P < 0.0001); they had higher disease activity and reduced renal function. Linear regression analysis revealed that low education was significantly associated with higher disease activity at enrollment into the 1000 Canadian Faces of Lupus database, after adjustment for age (at entry and at diagnosis), race/ethnicity, and sex (B 1.255 + 0.507 [SE], β = 0.115, P = 0.014). In our adjusted logistic regression models we were unable to demonstrate significant associations between education and SLE damage. Results did not change when varying the education variable. CONCLUSION: In this cohort, low education was associated cross-sectionally with higher disease activity and work disability, but not damage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".