Economic Costs of Myasthenia Gravis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: The objective of our study was to conduct a systematic literature review of economic costs (henceforth costs) associated with myasthenia gravis (MG). METHODS: We searched MEDLINE (through PubMed), CINAHL, Embase, PsycINFO, and Web of Science for studies reporting costs of MG published from inception up until March 18, 2020, without language restrictions. Two reviewers independently screened records for eligibility, extracted the data, and assessed included studies for risk of bias using the Newcastle-Ottawa Scale. Costs were inflated and converted to 2018 United States dollars ($). RESULTS: The search identified 16 articles for data extraction and synthesis. Estimates of costs of MG were found for samples from eight countries spanning four continents (Europe, North America, South America, and Asia). Across studies, the mean per-patient annual direct medical cost of illness was estimated at between $760 and $28,780, and cost per hospitalization between $2550 and $164,730. The indirect cost of illness was estimated at $80 and $3550. Costs varied considerably by patient characteristics, and drivers of the direct medical cost of illness included intravenous immunoglobulin and plasma exchange, myasthenic crisis, mechanical ventilatory support, and hospitalizations. CONCLUSIONS: We show that the current body of literature of costs of MG is sparse, limited to a few geographical settings and resource categories, mostly dated, and subject to non-trivial variability, both within and between countries. Our synthesis will help researchers and decision-makers identify gaps in the local health economic context of MG and inform future cost studies and economic evaluations in this patient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".