Economic evaluation of damage accrual in an international SLE inception cohort using a multi-state model approach
Bibliographic record
Abstract
OBJECTIVES: There is a paucity of data regarding healthcare costs associated with damage accrual in systemic lupus erythematosus (SLE). We describe costs associated with damage states across the disease course using multi-state modeling. METHODS: Patients from 33 centres in 11 countries were enrolled in the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort within 15 months of diagnosis. Annual data on demographics, disease activity, damage (SLICC/American College of Rheumatology (ACR) Damage Index [SDI]), hospitalizations, medications, dialysis, and selected procedures were collected. Ten-year cumulative costs (Canadian dollars) were estimated by multiplying annual costs associated with each SDI state by the expected state duration using a multi-state model. RESULTS: 1687 patients participated, 88.7% female, 49.0% of Caucasian race/ethnicity, mean age at diagnosis 34.6 years (SD 13.3), and mean follow up 8.9 years (range 0.6-18.5). Annual costs were higher in those with higher SDIs (SDI ≥ 5: $22 006 2019 CDN, 95% CI $16 662, $27 350 versus SDI=0: $1833, 95% CI $1134, $2532). Similarly, 10-year cumulative costs were higher in those with higher SDIs at the beginning of the 10-year interval (SDI ≥ 5: $189 073, 95% CI $142 318, $235 827 versus SDI=0: $21 713, 95% CI $13 639, $29 788). CONCLUSION: Patients with the highest SDIs incur 10-year cumulative costs that are almost 9-fold higher than those with the lowest SDIs. By estimating the damage trajectory and incorporating annual costs, damage can be used to estimate future costs, critical knowledge for evaluating the cost-effectiveness of novel therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".