'MiLES' population-based survey of the incidence and prevalence of systemic lupus erythematosus in Southeastern Michigan
Bibliographic record
Abstract
We estimated the incidence and prevalence of systemic lupus erythematosus (SLE) in a sociodemographically diverse southeastern Michigan source population of 2.4 million. SLE cases fulfilling American College of Rheumatology (ACR)SLE classification criteria (primary case definition) or rheumatologist-judged SLE (secondary definition) and residing in Wayne or Washtenaw Counties during 2002 to 2004 were included. Case finding was performed from six source types, including hospitals and private specialists. Age-standardized rates were computed and capture-recapture performed to estimate under-ascertainment of cases. Overall age-adjusted SLE incidence and prevalence per 100,000 were 5.5 (95% CI = 5.0 to 6.1) and 72.4 (95% CI = 70.4 to 74.4); capture-recapture adjusted estimates were 5.6 (95% CI = 5.1 to 6.2) and 71.8 (95% CI = 69.8 to 73.8). For all women the incidence was 9.3/100,000; prevalence was 128/100,000. SLE prevalence was 2.4-fold higher in blacks than whites, and 10-fold higher in women than men. Among the incident cases (ACR definition), mean age (± SD) at diagnosis overall was 39.2 ± 16.6 years. Blacks had a higher proportion of renal disease and end-stage renal disease (40.3% and 15.1%) versus whites (18.7% and 4.5%); blacks with renal disease were diagnosed with SLE at significantly younger age (33.9 ± 15.0 vs. whites 41.9 ± 21.3, P = 0.04).
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".