Serious infection incidence rates in pediatric systemic lupus erythematosus according to medication use
Bibliographic record
Abstract
We investigated incidence rates of serious infections among children with systemic lupus erythematosus (SLE) and lupus nephritis (LN) enrolled in Medicaid, the US health insurance program for low-income children and parents. We identified all children aged 5 to <18 years with SLE (≥3 ICD-9 codes of 710.0, each >30 days apart) and LN (≥2 ICD-9 codes for renal disease on/after SLE diagnosis) in the Medicaid Analytic eXtract (MAX) from 2000 to 2006. This dataset contains all outpatient and inpatient Medicaid claims for enrollees in 47 US states and the District of Columbia. Filled prescriptions were documented and patients were classified as new users of hydroxychloroquine (HCQ), corticosteroids (CS) and immunosuppressants (IS). We identified serious infections from hospital discharge diagnosis codes for all infections, and for specific subtypes of infections (bacterial, fungal and viral). We calculated incidence rates per 100 person-years (PY) overall and by medication subgroup. Incidence rate ratios (IRR) (95% CI) were calculated comparing CS, IS and CS+IS with HCQ alone using Poisson models, adjusted for age, sex and duration of enrollment in Medicaid. Among the 2,403 children with SLE who were new medication users, there were 316 serious infections in 2,215 PY. Incidence rates for all serious infections requiring hospitalization varied between 10/100 PY for SLE and 25.7/100 PY for LN. Among children with SLE receiving CS alone, incidence rates of serious infections were 3.5 times higher compared with those not receiving CS or IS. Among children receiving both CS and IS, overall serious infection incidence rates were twice as high compared with children not receiving either medication (Table 1 ). Infection remains a common complication of SLE and is associated with significant morbidity and mortality. We observed high rates of serious infections requiring hospitalization among children with SLE and LN. Those children receiving CS alone or in combination with IS had much higher rates of serious infections, compared with those children receiving HCQ alone.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".