P.105 Therapeutic options for changing the course of disease in generalized myasthenia gravis (gMG) and fiscal consequences for Canadian governments
Bibliographic record
Abstract
Background: Generalized myasthenia gravis (gMG) is a potentially life threatening chronic autoimmune disease that can impair patients’ ability to work effectively and increase reliance on public support benefits. A public economic framework was used to explore how treatment influences patients’ and caregivers’ economic activity, including tax revenues and public support in Canada. Methods: Natural history of gMG was simulated using a multi-state Markov cohort model. Health states were based on MG Activities of Daily Living (MG-ADL) total score in patients with AChR-Ab+ refractory gMG. Treatment, costs, and economic outcomes of patients taking efgartigimod were compared with alternative therapeutic options. Canadian public support benefits were based on official government sources. Results: Improved MG-ADL states predict higher workforce participation, lower rates of disability and less caregiving needs, resulting in higher tax revenues and less public support costs. Compared to alternative therapeutic options, efgartigimod is estimated to yield lifetime fiscal gains of $458,755 that exceed the incremental cost of $291,073, suggesting the Canadian government receives $1.6 for every $1.0 spent on efgartigimod for the treatment of gMG. Conclusions: Compared with alternative options, efgartigimod generated a positive fiscal return for the Canadian governments with additional savings from disease management, public benefits, and averted tax revenue losses.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.001 |
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".