A cost–utility analysis of dulaglutide versus insulin glargine as third-line therapy for Type 2 diabetes in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: The prevalence of Type 2 diabetes in Canada is estimated to be 7.6% and rising. Given the substantial economic burden associated with Type 2 diabetes treatment, optimizing healthcare expenditure is extremely important. In the present analysis, we evaluated the cost-effectiveness of dulaglutide 1.5 mg, a once-weekly glucagon-like peptide 1 agonist as third-line therapy relative to insulin glargine from the perspective of a Canadian healthcare payer. METHODS: A patient-level cost-utility model of Type 2 diabetes was developed to capture seven microvascular and macrovascular complications and severe and nonsevere hypoglycemia. Cohort characteristics and the relative efficacy of dulaglutide 1.5 mg and insulin glargine were derived from the AWARD-2 head-to-head trial, which was identified by systematic literature review. Cost data were derived from Canadian sources and expressed in 2016 Canadian dollars (CAD), and future cost and quality-adjusted life expectancy (QALE) estimates were discounted at 1.5% per annum. One-way and probabilistic sensitivity analyses were conducted. RESULTS: Based on the AWARD-2 trial, relative to insulin glargine, dulaglutide 1.5 mg was projected to increase QALE by 0.38 quality-adjusted life years and increase costs by CAD 19,773, resulting in an incremental cost-effectiveness ratio of CAD 52,580 per quality-adjusted life year gained. CONCLUSION: A computer simulation analysis showed that dulaglutide 1.5 mg would likely be cost-effective relative to insulin glargine in patients with Type 2 diabetes inadequately controlled on metformin and sulfonylurea in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it