Cost-effectiveness of switching to biphasic insulin aspart from human premix insulin in a US setting
Bibliographic record
Abstract
OBJECTIVES: To evaluate the cost-effectiveness of switching to biphasic insulin aspart (BIAsp 30) from human premix insulin for type 2 diabetes patients in the United States (US) setting. METHODS: The previously published and validated IMS Core Diabetes Model was used to project life expectancy, quality-adjusted life expectancy (QALE) and costs over 30 years. Patient characteristics and treatment effects were based on Canadian patients included the IMPROVE observational study (n = 311). Mean glycohaemoglobin (HbA(1c)) was 8.4%, duration of diabetes 16 years and prevalence of complications high at baseline. Simulations were conducted from the perspective of a third-party payer, with costs accounted in 2008 US dollars ($). RESULTS: BIAsp 30 was projected to improve life expectancy by 0.202 years and QALE by 0.301 quality-adjusted life-years (QALYs), due to a reduced incidence of most diabetes-related complications. BIAsp 30 was associated with increased lifetime direct medical costs ($76,517 vs. 67,518) and an incremental cost-effectiveness ratio of $29,870 per QALY gained. Long-term outcomes were sensitive to the impact of BIAsp 30 on hypoglycaemia and changes in HbA(1c). CONCLUSIONS: BIAsp 30 may represent a cost-effective treatment option in the US setting for advanced type 2 diabetes patients experiencing poor glycaemic control or hypoglycaemia on human premix insulin. LIMITATIONS: The application of treatment effect data derived from a Canadian cohort to the US setting was a limitation of the cost-effectiveness analysis. The findings of this cost-effectiveness analysis are not applicable to insulin-naïve diabetes patients.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.003 | 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".