Changes in the Incidence of Endstage Renal Disease Due to Lupus Nephritis in the United States, 1996–2004
Bibliographic record
Abstract
OBJECTIVE: To determine if the incidence of endstage renal disease (ESRD) due to lupus nephritis has decreased from 1996 to 2004. METHODS: Patients age 15 years or older with incident ESRD due to lupus nephritis in 1996-2004 and living in one of the 50 United States or the District of Columbia were identified using the US Renal Data System, a national population-based registry of all patients receiving renal replacement therapy for ESRD. Incidence rates were computed for each calendar year, using population estimates of the US census as denominators. RESULTS: Over the 9-year study period, 9199 new cases of ESRD due to lupus nephritis were observed. Incidence rates, adjusted to the age, sex, and race composition of the US population in 2000, were 4.4 per million in 1996 and 4.9 per million in 2004. Compared to the pooled incidence rate in 1996-1998, the relative risk of ESRD due to lupus nephritis in 1999-2000 was 0.99 (95% CI 0.93-1.06), in 2001-2002 was 0.99 (95% CI 0.92-1.06), and in 2003-2004 was 0.96 (95% CI 0.89-1.02). Findings were similar in analyses stratified by sex, age group, race, and socioeconomic status. CONCLUSION: There was no decrease in the incidence of ESRD due to lupus nephritis between 1996 and 2004. This may reflect the limits of effectiveness of current treatments, or limitations in access, use, or adherence to treatment.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".