D.2 Cost-effectiveness analysis of efgartigimod vs chronic IVIg for treatment of patients with generalized myasthenia gravis in Canada
Bibliographic record
Abstract
Background: Efgartigimod is a human IgG1 antibody Fc fragment recently approved by Health Canada for patients with acetylcholine receptor antibody positive (AChR-Ab+) generalized myasthenia gravis (gMG). We assessed cost-effectiveness of efgartigimod vs chronic IVIg for adult patients with AChR-Ab+ gMG. Methods: A Markov model estimated costs (treatment and administration, disease monitoring, complications from chronic corticosteroid use, exacerbation and crisis management, adverse events, end-of-life care) and benefits (quality-adjusted life-years [QALYs]). The analysis was conducted from the Canadian healthcare system perspective. Health state transition probabilities were estimated using data from ADAPT, ADAPT+, and a network meta-analysis comparing efgartigimod with chronic IVIg. Utility values were obtained from MyRealWorld MG. Patients with MG-ADL ≥5 who did not die/discontinue were assumed to receive treatment every 4 weeks or every 3 weeks over the lifetime horizon. Results: Over the lifetime horizon, efgartigimod and chronic IVIg were predicted to have total discounted QALYs of 16.80 and 13.35, and total discounted costs of $1,913,294 and $2,170,315, respectively. Efgartigimod dominated chronic IVIg with incremental QALYs of 3.45 and cost savings of $257,020 over the lifetime horizon. Conclusions: Efgartigimod may provide greater benefit at lower costs than chronic IVIg for Canadian patients with AChR-Ab+ gMG, with substantial healthcare system savings over the lifetime horizon.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".