Cost-utility of Intravenous Immunoglobulin (IVIG) compared with corticosteroids for the treatment of Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) in Canada
Bibliographic record
Abstract
OBJECTIVES: Intravenous immunoglobulin (IVIG) has demonstrated improvement in chronic inflammatory demyelinating polyneuropathy (CIDP) patients in placebo controlled trials. However, IVIG is also much more expensive than alternative treatments such as corticosteroids. The objective of the paper is to evaluate, from a Canadian perspective, the cost-effectiveness of IVIG compared to corticosteroid treatment of CIDP. METHODS: A markov model was used to evaluate the costs and QALYs for IVIG and corticosteroids over 5 years of treatment for CIDP. Patients initially responding to IVIG could remain a responder or relapse every 12 week model cycle. Non-responding IVIG patients were assumed to be switched to corticosteroids. Patients on corticosteroids were at risk of a number of adverse events (fracture, diabetes, glaucoma, cataract, serious infection) in each cycle. RESULTS: Over the 5 year time horizon, the model estimated the incremental costs and QALYs of IVIG treatment compared to corticosteroid treatment to be $124,065 and 0.177 respectively. The incremental cost per QALY gained of IVIG was estimated to be $687,287. The cost per QALY of IVIG was sensitive to the assumptions regarding frequency and dosing of maintenance IVIG. CONCLUSIONS: Based on common willingness to pay thresholds, IVIG would not be perceived as a cost effective treatment for CIDP.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".