Economic evaluation of Avonex (interferon beta-la) in patients following a single demyelinating event,” Multiple Sclerosis
Bibliographic record
Abstract
Background: Interferon beta-1a (Avonex†) 30 mg, intramuscular (i.m.), once weekly is efficacious in delaying clinically definite multiple sclerosis (CDMS) following a single demyelinating event (SDE). This study determined the cost effectiveness of Avonex † compared to current treatment in delaying the onset of CDMS. Methods: A cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) were performed from Ministry of Health (MoH) and societal perspectives. For CEA, the outcome of interest was time spent in the pre-CDMS state, termed monosymptomatic life years (MLY) gained. For CUA, the outcome was quality-adjusted monosymptomatic life years (QAMLY) gained. A Markov model was developed with transitional probabilities and utilities derived from the literature. Costs were reported in 2002 Canadian dollars. Costs and outcomes were discounted at 5%. The time horizon was 12 years for the CEA, and 15 years for the CUA. All uncertainties were tested via univariate and multivariate sensitivity analyses. Results: In the CEA, the incremental cost of Avonex † per MLY gained was $53 110 and $44 789 from MoH and societal perspectives, respectively. In the CUA, the incremental cost of Avonex † per QAMLY gained was $227 586 and $189 286 from MoH and societal perspectives, respectively. Both models were sensitive to the probability of progressing to CDMS and the analytical time horizon. The CUA was sensitive to the utilities value. Conclusion: Avonex † may be considered as a reasonably cost-effective approach to treatment of patients experiencing an SDE. In addition, the overall incremental cost-effectiveness profile of Avonex † improves if treatment is initiated in pre-CDMS rather than waiting until CDMS.
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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 | 0.008 | 0.024 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".