Canadian Rheumatology Association Meeting, Victoria, British Columbia, Canada, March 28–31, 2012
Bibliographic record
Abstract
Objective: Worse outcomes in systemic lupus erythematosus (SLE) patients from lower socioeconomic strata (SES) have been well documented. Low SES is associated with higher chronic stress, which in turn may result in increased inflammation and immune dysfunction. We examined the relationship between SES, autoantibody frequency, and inflammation in SLE patients. Methods: Adult incident and prevalent SLE patients were enrolled in a prospective cohort at a single centre. Sociodemographic variables, data on health-related habits, diagnostic criteria, disease activity, autoantibodies, treatment, and damage were collected annually using standardized tools. SES was measured as educational level achieved and annual household income. Disease activity was evaluated using the Systemic Lupus Activity Measure (SLAM). Organ damage was measured using the SLICC/ACR Damage Index (SDI). Autoantibodies measured included antinuclear antibody, dsDNA antibody, extractable nuclear antigens and anti-phospholipid antibodies. Inflammation was measured using the erythrocyte sedimentation rate (ESR) score from the SLAM. Baseline data was analyzed, testing for differences in ESR score and total number of autoantibodies positive between income and education groups. Significant variables from univariate analyses were then included in multivariate regression models examining for predictors of total autoantibody frequency, ESR score, and organ damage. Results: Two hundred seventy-three patients were enrolled in the cohort, mean disease duration was 13.7 years, and mean age was 48.5 years. Ninety percent were female, 14% had incomes below the poverty line, while 51% had annual incomes >$50,000. Seventy-seven percent had completed high school. No associations were found between SES and autoantibody frequency. Less education and low income were associated with increased ESR scores (p< 0.001, p=0.035 respectively) in univariate analysis. Both income and education were predictors of higher ESR scores in linear regression (p=0.025 and p=0.047 respectively). Higher ESR score and lack of high school completion (p=0.032 and p=0.04 respectively) were predictors of SDI scores when total ACR score, age, and income were included in the regression model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".