Incidence and Prevalence of Juvenile Systemic Lupus Erythematosus in Korea: Data From the 2017 National Health Claims Database
Bibliographic record
Abstract
OBJECTIVE: The purpose of the present study was to investigate the prevalence and incidence of juvenile systemic lupus erythematosus (JSLE) in Korea. METHODS: The data were collected from the National Health Insurance Claims Database of Korea. JSLE was identified using the diagnostic code M32 from the Korean Standard Classification of Diseases. Patients between 5 and 18 years old, who had at least 1 claim for JSLE from January 1, 2016, to December 31, 2017, as final diagnosis, were analyzed in the study. For prevalent cases, patients who used, at least 1 time, any type of medical services with a diagnostic code of M32 were selected. For incident cases, patients who did not use medical services with the M32 code 1 year prior and who were newly registered in 2017 were defined. Change-point analysis was used to find the age at which changes in prevalence and incidence occurred. RESULTS: The prevalence of JSLE was 6.92 per 100,000 persons and the incidence of JSLE was 2.76 per 100,000 person-years in patients between 5 and 18 years old. The prevalence and incidence of JSLE were higher in females than in males. According to the change-point analysis, we found that the incidence and prevalence of female patients increased rapidly at the ages of 14 and 15 years, respectively. CONCLUSION: This Korean population-based epidemiological study of JSLE showed similar epidemiologic profiles to Asian population in other studies. The distribution of age, ethnicity, and pubertal status are important factors that influence population estimates of JSLE incidence and prevalence.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".