Incidence rates of systemic lupus erythematosus in the USA: estimates from a meta-analysis of the Centers for Disease Control and Prevention national lupus registries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To estimate the annual incidence rate of SLE in the USA. METHODS: A meta-analysis used sex/race/ethnicity-specific data spanning 2002-2009 from the Centers for Disease Control and Prevention network of four population-based state registries to estimate the incidence rates. SLE was defined as fulfilling the 1997 revised American College of Rheumatology classification criteria. Given heterogeneity across sites, a random effects model was employed. Applying sex/race/ethnicity-stratified rates, including data from the Indian Health Service registry, to the 2018 US Census population generated estimates of newly diagnosed SLE cases. RESULTS: The pooled incidence rate per 100 000 person-years was 5.1 (95% CI 4.6 to 5.6), higher in females than in males (8.7 vs 1.2), and highest among black females (15.9), followed by Asian/Pacific Islander (7.6), Hispanic (6.8) and white (5.7) females. Male incidence was highest in black males (2.4), followed by Hispanic (0.9), white (0.8) and Asian/Pacific Islander (0.4) males. The American Indian/Alaska Native population had the second highest race-specific SLE estimates for females (10.4 per 100 000) and highest for males (3.8 per 100 000). In 2018, an estimated 14 263 persons (95% CI 11 563 to 17 735) were newly diagnosed with SLE in the USA. CONCLUSIONS: A network of population-based SLE registries provided estimates of SLE incidence rates and numbers diagnosed in the USA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it