Hospitalization costs with degludec versus glargine U100 for patients with type 2 diabetes at high cardiovascular risk: Canadian costs applied to SAEs from a randomized outcomes trial
Bibliographic record
Abstract
Objectives The present cost–consequence analysis compared estimated hospitalization costs in a Canadian setting with insulin degludec (degludec) versus insulin glargine 100 units/mL (glargine U100) in patients with type 2 diabetes (T2D) at high cardiovascular (CV) risk.Methods Medical terms were mapped across the different vocabularies, in order to assign unit costs from eligible hospital abstracts in Canadian Institute for Health Information data (International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Canada) to serious adverse events (SAEs; Medical Dictionary for Regulatory Activities) from the randomized DEVOTE trial comparing the two insulins degludec and glargine. Mean annual costs of SAE-related hospitalizations were estimated by treatment, the cost difference (degludec − glargine U100) was bootstrapped to compute confidence intervals (CIs) and p-values, and the cost ratio (degludec/glargine U100) was estimated using a Tweedie distribution.Results The mean annual cost per patient for SAE-related hospitalizations was 4,074 CAD with degludec and 4,569 CAD with glargine U100 (cost difference: −495, 95% confidence interval [CI]: −966; −24, p = .039), for a cost ratio of 0.89 (95% CI: 0.81; 0.98, p = .016). Overall, cost ratios from sensitivity analyses varying individual methodological assumptions were consistent with the main analysis. Of the system organ classes from DEVOTE SAEs, cardiac disorders were the largest contributor to the costs savings with degludec versus glargine U100.Conclusions In patients with T2D at high CV risk, our findings suggest that there are likely to be lower hospitalization costs with degludec versus glargine U100 based on the SAEs observed in DEVOTE and in a Canadian setting.
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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) | 0.001 | 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".