Cost-minimization analysis comparing intravenous immunoglobulin (IVIg) with plasma exchange (PLEX) in the management of patients with myasthenia gravis: different perspectives for different payers
Bibliographic record
Abstract
Introduction: A cost-minimization analysis (CMA) was performed comparing IVIg and PLEX in the management of patients with exacerbation of myasthenia gravis (MG). Methods: This study combines Ontario-based health costing data with clinical data from a randomized clinical trial where patients with moderate/severe MG received either IVIg or PLEX. The CMA was undertaken under the perspective of a public health care insurer and under the perspective of a tertiary hospital payer. Results: The IVIg group (n=32) was comparable with the PLEX group (n=38) regarding demographics, disease characteristics and severity. PLEX was less costly than IVIg among patients with body mass index (BMI) ≤15.7 Kg/m2, under the perspective of a public health care insurer (CAN$6,271.18 versus CAN$8,309.72, p<0.0001). However, PLEX was more costly than IVIg under the perspective of the hospital payer when the costs of blood products were excluded (CAN$4,815.36 versus CAN$1,486.12, p<0.0001). Conclusions: PLEX may be a short-term cost-minimizing therapy when compared with IVIg for treatment of MG exacerbation among patients with BMI ≤15.7 Kg/m2, under the perspective of a public health care insurer. However, when the costs of blood products are absorbed by a third party, the hospital administration may see IVIg as a more attractive therapeutic alternative.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".