Glomerular Filtration Rate and Associated Risks ofCardiovascular Events, Mortality, and Severe Hypoglycemia in Patients with Type 2 Diabetes:Secondary Analysis (DEVOTE 11)
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Bibliographic record
Abstract
INTRODUCTION: The associations of chronic kidney disease (CKD) severity, cardiovascular disease (CVD), and insulin with the risks of major adverse cardiovascular events (MACE), mortality, and severe hypoglycemia in patients with type 2 diabetes (T2D) at high cardiovascular (CV) risk are not known. This secondary, pooled analysis of data from the DEVOTE trial examined whether baseline glomerular filtration rate (GFR) categories were associated with a higher risk of these outcomes. METHODS: DEVOTE was a treat-to-target, double-blind trial involving 7637 patients with T2D at high CV risk who were randomized to once-daily treatment with either insulin degludec (degludec) or insulin glargine 100 units/mL (glargine U100). Patients with estimated GFR data at baseline (n = 7522) were analyzed following stratification into four GFR categories. RESULTS: ), or both were at higher risk of MACE, CV death, and all-cause mortality. Only CKD was associated with a higher rate of severe hypoglycemia, and the risk of MACE was higher in patients with CVD than in those with CKD (P = 0.0003). There were no significant interactions between randomized treatment and GFR category. CONCLUSION: The risks of MACE, CV death, and all-cause mortality were higher with lower baseline GFR and with prior CVD, CKD, or both. The relative effects of degludec versus glargine U100 on outcomes were consistent across baseline GFR categories, suggesting that the lower rate of severe hypoglycemia associated with degludec use versus glargine U100 use was independent of baseline GFR category. FUNDING: Novo Nordisk.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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