Medical costs and healthcare resource use in patients with lupus nephritis and neuropsychiatric lupus in an insured population
Bibliographic record
Abstract
OBJECTIVE: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that can affect multiple organ systems, including the kidneys (lupus nephritis) and the central nervous system (neuropsychiatric lupus, or NPSLE). The healthcare costs and resource utilization associated with treating lupus nephritis and NPSLE in a large US managed care plan were studied. METHODS: SLE subjects ≥18 years of age and with claims-based evidence of nephritis or neuropsychiatric conditions were identified from a health plan database. An index date was set as a randomly drawn date from all qualifying claims during 2003-2008 for study subjects. Subjects were matched on the basis of demographic and clinical characteristics to unaffected controls. Costs and resource use were determined during a fixed 12-month post-index period. RESULTS: Nine hundred and seven lupus nephritis subjects were matched to controls, and 1062 subjects with NPSLE were matched to controls. Mean overall post-index healthcare costs were significantly higher among subjects with lupus nephritis in comparison to matched controls ($33,472 vs $5347, p < 0.001). Similarly, mean overall post-index healthcare costs were significantly higher among subjects with NPSLE compared to controls ($30,341 vs $4646, p < 0.001). Subjects with lupus nephritis or NPSLE had higher mean post-index numbers of ambulatory visits, specialist visits, emergency department visits and inpatient hospital stays, compared to controls (all p < 0.001). LIMITATIONS: Additional research, such as medical chart review, could provide validation for the claims-based identification of lupus nephritis and NPSLE subjects. Also, indirect costs were not evaluated in this study. CONCLUSION: Subjects with lupus nephritis or NPSLE have high costs and resource use, compared to unaffected controls.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".