Cost-Effectiveness of Repository Corticotropin Injection for the Treatment of Acute Exacerbations in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Gel), plasmapheresis (PMP), and intravenous immunoglobulin (IVIg) are alternative therapies for MS relapse. There is a dearth of economic assessments of these therapies for the acute exacerbations of MS. This study estimated the cost-effectiveness of RCI compared to PMP or IVIg. METHODS: A Markov state-transition model compared outcomes (costs, relapses, remission, and utilities) with RCI versus PMP or IVIg for the acute exacerbations in MS. The model was developed from the United States (US) payer and societal perspectives over one to three years. Patients initiated on alternative therapies were evaluated in one-day increments for the first 30 days during treatment. The model assumes the natural history of MS after treatment in the first month, adjusting for the effect of treatment. Incremental cost-effectiveness ratios (ICERs) were estimated as cost per quality-adjusted life-year (QALY) gained. The uncertainty in model parameters was evaluated in probabilistic sensitivity analyses. RESULTS: In the base case, RCI has an ICER of USD 42,078 per QALY compared to PMP over one year from the payer perspective and is dominant over two and three years; RCI is dominant compared to PMP from the societal perspective over all three years. Compared to IVIg, RCI is a dominant strategy from both payer and societal perspectives over all three years. Probabilistic sensitivity analysis supports the base case findings, suggesting that RCI may be cost-effective versus PMP and IVIg for acute exacerbations in MS. CONCLUSION: RCI is a cost-effective alternative treatment for MS relapses compared to PMP and IVIg from the US payer and societal perspectives.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".