Heart failure with insulin degludec versus glargine U100 in patients with type 2 diabetes at high risk of cardiovascular disease: DEVOTE 14
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is a common cardiovascular complication of type 2 diabetes (T2D). This secondary analysis investigated baseline factors and treatment differences associated with risk of hospitalization for HF (hHF), and the possible association between severe hypoglycemia and hHF. METHODS: DEVOTE was a treat-to-target, double-blind cardiovascular outcomes trial in patients (n = 7637) with T2D and high cardiovascular risk randomized to insulin degludec (degludec) or insulin glargine 100 units/mL (glargine U100). The main endpoint of this secondary analysis was time to first hHF (standardized MedDRA Query definition). Severe hypoglycemia was adjudicated (American Diabetes Association definition). The main endpoint and the temporal association between severe hypoglycemia and hHF were analyzed with a Cox proportional hazards regression model. Predictors of time to first hHF were identified using baseline variables. RESULTS: , p ≤ 0.0001) was the strongest predictor of future hHF events. The risk of hHF significantly increased after (HR 2.2), and within a week after (HR 11.1), experiencing a severe hypoglycemic episode compared with before an episode. CONCLUSIONS: In patients with T2D and high cardiovascular risk there were no treatment differences in terms of hHF. Prior HF was the strongest predictor of future hHF events, and there was an association between severe hypoglycemia and subsequent hHF. Further research should evaluate whether the risk of hHF can be modified by treatments aimed at reducing hypoglycemia. Trial Registration NCT01959529.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".