Cost-effectiveness of self-monitoring of blood glucose in patients with type 2 diabetes mellitus managed without insulin
Bibliographic record
Abstract
BACKGROUND: The benefits of self-monitoring blood glucose levels are unclear in patients with type 2 diabetes mellitus who do not use insulin, but there are considerable costs. We sought to determine the cost effectiveness of self-monitoring for patients with type 2 diabetes not using insulin. METHODS: We performed an incremental cost-effectiveness analysis of the self-monitoring of blood glucose in adults with type 2 diabetes not taking insulin. We used the United Kingdom Prospective Diabetes Study (UKPDS) model to forecast diabetes-related complications, corresponding quality-adjusted life years and costs. Clinical data were obtained from a systematic review comparing self-monitoring with no self-monitoring. Costs and utility decrements were derived from published sources. We performed sensitivity analyses to examine the robustness of the results. RESULTS: Based on a clinically modest reduction in hemoglobin A(1C) of 0.25% (95% confidence interval 0.15-0.36) estimated from the systematic review, the UKPDS model predicted that self-monitoring performed 7 or more times per week reduced the lifetime incidence of diabetes-related complications compared with no self-monitoring, albeit at a higher cost (incremental cost per quality-adjusted life year $113,643). The results were largely unchanged in the sensitivity analysis, although the incremental cost per quality-adjusted life year fell within widely cited cost-effectiveness thresholds when testing frequency or the price per test strip was substantially reduced from the current levels. INTERPRETATION: For most patients with type 2 diabetes not using insulin, use of blood glucose test strips for frequent self-monitoring (>or= 7 times per week) is unlikely to represent efficient use of finite health care resources, although periodic testing (e.g., 1 or 2 times per week) may be cost-effective. Reduced test strip price would likely also improve cost-effectiveness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".