Pharmacoeconomics Evaluation of Insulin Analogs versus Human Insulin for Diabetes:A Systematic Review
Bibliographic record
Abstract
OBJECTIVE:To systematically review the pharmacoeconomics of insulin analogs versus human insulin [NPH] so as to provide evidence for relevant health decision-making and clinical treatment. METHODS:Literatures about the pharmacoeconomics evaluation of insulin analogs versus human insulin were retrieved from Chinese and English literature database. Basic information,data sources and results of included studies were analyzed and reviewed. RESULTS:27 studies in 16 published papers carried out in Canada,USA,European,Australia and China were included in the review. The results of studies were significantly inconsistent,which was perhaps mainly due to the different data source,model selection,time horizon and hypothesis. However,the public health institutes in Canada,UK,Germany and Australia had reported highly suspiciousness on the economics of insulin analogs for diabetes patients,especially for type Ⅱ diabetes. CONCLUSIONS:In lack of powerful evidence,it has not reached an agreement about the economics of insulin analogs for diabetes. It can not be decided that the economics of insulin analogs is better than human insulin based on present studies. In developed countries,insulin analogs are recommended with caution in reimbursement policies. As China is a developing country,diabetes patients should select appropriate regimes even more cautiously according to local healthcare system,personal disease characteristics and affordability. More high-quality studies about economic evaluation of insulin analogs and human insulin on the basis of national condition are required to provide evidence for the government to allocate medical resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".