Health Economic Evaluation of Type 2 Diabetes Mellitus: A Clinical Practice Focused Review
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2D) is a growing healthcare burden primarily due to long-term complications. Strict glycemic control helps in preventing complications, and early introduction of insulin may be more cost-effective than maintaining patients on multiple oral agents. This is an expert opinion review based on English peer-reviewed articles (2000–2012) to discuss the health economic consequences of T2D treatment intensification. T2D costs are driven by inpatient care for treatment of diabetes complications (40%–60% of total cost), with drug therapy for glycemic control representing 18% of the total cost. Insulin therapy provides the most improved glycemic control and reduction of complications, although hypoglycemia and weight gain may occur. Early treatment intensification with insulin analogs in patients with poor glycemic control appears to be cost-effective and improves clinical outcomes. Key Messages • Type 2 diabetes mellitus is a growing burden on healthcare services. • Despite the high cost of drug therapy versus diet and lifestyle interventions, treatment intensification with insulin analog therapy is a cost-effective strategy for improving clinical outcomes in patients with poor glycemic control.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".