Incidence of Systemic Lupus Erythematosus and Lupus Nephritis in Denmark: A Nationwide Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of systemic lupus erythematosus (SLE) and SLE with concomitant or subsequent lupus nephritis (LN) in Denmark during 1995-2011, using data from the Danish National Patient Registry (NPR). METHODS: To assess the incidence of SLE, we identified all persons aged ≥ 18 years in the NPR with at least 1 International Classification of Diseases, 10th ed (ICD-10) code of SLE and at least 365 days of followup under this diagnosis. Identification of LN cases was based on fulfillment of these criteria and ≥ 1 registration under an ICD-10 code of nephritis concomitantly with or after first SLE registration. RESULTS: The overall annual incidence rate per 100,000 for SLE was 2.35 (95% CI 2.24-2.49); 0.69 (95% CI 0.60-0.78) for men and 3.96 (95% CI 3.75-4.17) for women. For LN, the mean annual incidence rate per 100,000 was estimated to be 0.45 (95% CI 0.38-0.53); 0.20 (95% CI 0.13-0.28) for men and 0.69 (95% CI 0.57-0.83) for women. The differences in SLE incidence rates between sexes decreased by age, and the incidence did not differ between men and women after the age of 60 years for LN. The estimated incidences showed no trends by calendar time. Estimated overall point prevalence (December 31, 2011) per 100,000 was 45.2 (95% CI 43.3-47.4) and 6.4 (95% CI 5.7-7.2) for SLE and LN, respectively. CONCLUSION: Our Danish population-based data showed a stable incidence of SLE and LN. As expected, we found higher incidence rates among women than among men, particularly in younger persons.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".