Health and economic impact of combining metformin with nateglinide to achieve glycemic control: Comparison of the lifetime costs of complications in the U.K
Bibliographic record
Abstract
BACKGROUND: To reduce the likelihood of complications in persons with type 2 diabetes, it is critical to control hyperglycaemia. Monotherapy with metformin or insulin secretagogues may fail to sustain control after an initial reduction in glycemic levels. Thus, combining metformin with other agents is frequently necessary. These analyses model the potential long-term economic and health impact of using combination therapy to improve glycemic control. METHODS: An existing model that simulates the long-term course of type 2 diabetes in relation to glycosylated haemoglobin (HbA1c) and post-prandial glucose (PPG) was used to compare the combination of nateglinide with metformin to monotherapy with metformin. Complication rates were estimated for major diabetes-related complications (macrovascular and microvascular) based on existing epidemiologic studies and clinical trial data. Utilities and costs were estimated using data collected in the United Kingdom Prospective Diabetes Study (UKPDS). Survival, life years gained (LYG), quality-adjusted life years (QALY), complication rates and associated costs were estimated. Costs were discounted at 6% and benefits at 1.5% per year. RESULTS: Combination therapy was predicted to reduce complication rates and associated costs compared with metformin. Survival increased by 0.39 (0.32 discounted) and QALY by 0.46 years (0.37 discounted) implying costs of pound 6,772 per discounted LYG and pound 5,609 per discounted QALY. Sensitivity analyses showed the results to be consistent over broad ranges. CONCLUSION: Although drug treatment costs are increased by combination therapy, this cost is expected to be partially offset by a reduction in the costs of treating long-term diabetes complications.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".