Incidence and prevalence of adult systemic lupus erythematosus in a large US managed-care population
Bibliographic record
Abstract
OBJECTIVE: The objective of this paper is to determine the incidence and prevalence of adult systemic lupus erythematosus (SLE) in a large US managed-care population. METHODS: Subject inclusion in the incidence cohort required a medical claim with an SLE diagnosis and a service date from 2003 to 2008 that satisfied the following criteria: 1) ≥18 years on service date; 2) continuously enrolled for 24 months before and 12 months after service date; 3) in the 12 months after service date, ≥ one inpatient claim or ≥ two office or ER visits with an SLE diagnosis; 4) no SLE diagnosis 24 months prior to service date; and 5) no SLE medications 12 months prior to service date. Prevalence cohort subjects were identified using a similar algorithm and were not required to satisfy criteria 4) and 5). RESULTS: A total of 1,557 subjects were included in the incidence cohort, and 15,396 were included in the prevalence cohort. The overall age- and gender-adjusted SLE incidence rate (2003-2008) was 7.22 cases per 100,000 person-years. The annual prevalence of SLE (per 100,000 individuals) varied from 81.07 in 2003 to 102.94 in 2008. CONCLUSION: The SLE incidence in this large managed-care plan with geographic diversity was slightly higher than previous estimates, and the prevalence was within the range of previous estimates.
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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 | 0.000 | 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".