Economic Evaluation of Lupus Nephritis in the Systemic Lupus International Collaborating Clinics Inception Cohort Using a Multistate Model Approach
Bibliographic record
Abstract
OBJECTIVE: Little is known about the long-term costs of lupus nephritis (LN). The costs were compared between patients with and without LN using multistate modeling. METHODS: Patients from 32 centers in 11 countries were enrolled in the Systemic Lupus International Collaborating Clinics inception cohort within 15 months of diagnosis and provided annual data on renal function, hospitalizations, medications, dialysis, and selected procedures. LN was diagnosed by renal biopsy or the American College of Rheumatology classification criteria. Renal function was assessed annually using the estimated glomerular filtration rate (GFR) or estimated proteinuria. A multistate model was used to predict 10-year cumulative costs by multiplying annual costs associated with each renal state by the expected state duration. RESULTS: A total of 1,545 patients participated; 89.3% were women, the mean ± age at diagnosis was 35.2 ± 13.4 years, 49% were white, and the mean followup duration was 6.3 ± 3.3 years. LN developed in 39.4% of these patients by the end of followup. Ten-year cumulative costs were greater in those with LN and an estimated glomerular filtration rate (GFR) <30 ml/minute ($310,579 2015 Canadian dollars versus $19,987 if no LN and estimated GFR >60 ml/minute) or with LN and estimated proteinuria >3 gm/day ($84,040 versus $20,499 if no LN and estimated proteinuria <0.25 gm/day). CONCLUSION: Patients with estimated GFR <30 ml/minute incurred 10-year costs 15-fold higher than those with normal estimated GFR. By estimating the expected duration in each renal state and incorporating associated annual costs, disease severity at presentation can be used to anticipate future health care costs. This is critical knowledge for cost-effectiveness evaluations of novel therapies.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".