Assessing the Costs of Neuropsychiatric Disease in the Systemic Lupus International Collaborating Clinics Cohort Using Multistate Modeling
Bibliographic record
Abstract
OBJECTIVE: To estimate direct and indirect costs associated with neuropsychiatric (NP) events in the Systemic Lupus International Collaborating Clinics inception cohort. METHODS: NP events were documented annually using American College of Rheumatology definitions for NP events and attributed to systemic lupus erythematosus (SLE) or non-SLE causes. Patients were stratified into 1 of 3 NP states (no, resolved, or new/ongoing NP event). Change in NP status was characterized by interstate transition rates using multistate modeling. Annual direct costs and indirect costs were based on health care use and impaired productivity over the preceding year. Annual costs associated with NP states and NP events were calculated by averaging all observations in each state and adjusted through random-effects regressions. Five- and 10-year costs for NP states were predicted by multiplying adjusted annual costs per state by expected state duration, forecasted using multistate modeling. RESULTS: A total of 1,697 patients (49% White race/ethnicity) were followed for a mean of 9.6 years. NP events (n = 1,971) occurred in 956 patients, 32% attributed to SLE. For SLE and non-SLE NP events, predicted annual, 5-, and 10-year direct costs and indirect costs were higher in new/ongoing versus no events. Direct costs were 1.5-fold higher and indirect costs 1.3-fold higher in new/ongoing versus no events. Indirect costs exceeded direct costs 3.0 to 5.2 fold. Among frequent SLE NP events, new/ongoing seizure disorder and cerebrovascular disease accounted for the largest increases in annual direct costs. For non-SLE NP events, new/ongoing polyneuropathy accounted for the largest increase in annual direct costs, and new/ongoing headache and mood disorder for the largest increases in indirect costs. CONCLUSION: Patients with new/ongoing SLE or non-SLE NP events incurred higher direct and indirect costs.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".