Assessing the Costs of Neuropsychiatric Disease in the Systemic Lupus International Collaborating Clinics (SLICC) Cohort using Multistate Modelling
Bibliographic record
Abstract
OBJECTIVE: To estimate direct and indirect costs (DC, IC) associated with neuropsychiatric (NP) events in the SLICC Inception Cohort. METHODS: NP events were documented annually using ACR NP definitions and attributed to SLE or non-SLE causes. Patients were stratified into one of three NP states (no, resolved, or new/ongoing NP event). Change in NP status was characterized by inter-state transition rates using multi-state modelling. Annual DC and IC were based on healthcare 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 modelling. RESULTS: 1697 patients (49% White race/ethnicity) were followed a mean of 9.6 years. NP events (n=1971) occurred in 956 patients, 32% attributed to SLE. For SLE and non-SLE NP events, predicted annual, five, and 10-year DC and IC were higher in new/ongoing versus no events. DC were 1.5-fold higher and IC 1.3-fold higher in new/ongoing versus no events. IC exceeded DC 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 DC. For non-SLE NP events, new/ongoing polyneuropathy accounted for the largest increase in annual DC and new/ongoing headache and mood disorder for the largest increases in IC. CONCLUSION: Patients with new/ongoing SLE or non-SLE NP events incurred higher DC and IC.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 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".