National Trends in Pediatric Systemic Lupus Erythematosus Hospitalization in the United States: 2000–2009
Bibliographic record
Abstract
OBJECTIVE: In the setting of recent healthcare advances and emphasis on reduced spending, we aimed to characterize US trends in inpatient healthcare use and mortality for pediatric systemic lupus erythematosus (SLE). METHODS: We performed a retrospective, serial, cross-sectional analysis of the national Kids' Inpatient Database (for 2000, 2003, 2006, and 2009). We identified patients with SLE aged 2 to 21 years using an International Classification of Diseases, 9th revision (ICD-9) code of 710.0 listed as a discharge diagnosis. Using sampling weights, we estimated trends in hospitalization, inpatient mortality, procedure rates, and length of stay (LOS). We analyzed patient and hospital-specific risk factors for mortality and LOS, and compared those outcomes to those without SLE. RESULTS: We identified 26,903 estimated pediatric SLE hospitalizations. The hospitalization rate of 8.6 (95% CI 7.6-9.6) per 100,000 population and mean LOS of 5.9 days (95% CI 5.6-6.2) were stable over time. We found a significant downward trend in mortality, decreasing from 1% to 0.6% (p = 0.04), which paralleled a less pronounced trend for those without SLE. The rate of dialysis, blood transfusions, and vascular catheterization procedures increased. Patients with SLE nephritis and non-white race were at risk for increased healthcare use and death. CONCLUSION: Pediatric SLE hospitalization rate and LOS remained stable, but inpatient mortality decreased as the rate of common therapeutic procedures increased. More research is needed to understand the drivers of these relationships.
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 | 0.002 |
| 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 | 0.001 |
| 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".