COST-EFFECTIVENESS OF INTERFERON BETA-1B IN SLOWING MULTIPLE SCLEROSIS DISABILITY PROGRESSION
Bibliographic record
Abstract
OBJECTIVE: To estimate the cost-effectiveness (CE) of interferon beta-1b (IFN beta-1b) in slowing disability progression in persons with relapsing-remitting multiple sclerosis (RRMS). METHODS: Treatment program costs and health outcomes are modeled for cohorts of 1,000 females and 1,000 males followed 40 years from onset. Fifteen scenarios model MS natural history progression, treatment efficacy, direct treatment costs, and MS healthcare costs. A single randomized placebo-controlled trial of IFN beta-1b found reduced disease activity by MRI, reduced frequency and severity of exacerbations, and a tendency toward slower disability progression. Disability years avoided are modeled as the primary health outcome analyzed. A ministry of health (MOH) perspective is adopted, using Nova Scotia population-based data. Annual IFN beta-1b direct treatment costs (Can $16,685) are high relative to both MOH healthcare costs per person with MS (Can $2,000) and estimated MOH costs avoided. RESULTS: Given "reference case" assumptions for women with RRMS, treatment reduces lifetime disability years by 10%. Cost per disability year avoided before discounting is Can $189,230 (US $124,892), and Can $274,842 (US $181,395) after discounting at 5%. Estimates for alternative scenarios vary greatly, leaving main findings unchanged. CONCLUSIONS: Using the Expanded Disability Status Scale, cost per disability year avoided due to interferon beta-1b treatment in RRMS is quite high. Comparable CE estimates, using MS-specific or generic health-related quality-of-life outcome measures, are even higher. Further research is required to better measure treatment effects, modification of MS natural history, and net societal costs of IFN beta-1b in RRMS.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".