The clinical burden of type 2 diabetes in patients with acute coronary syndromes: Prognosis and implications for short- and long-term management
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) is associated with increased morbidity and mortality in patients with acute coronary syndromes (ACS). Cardiometabolic risk factors, including hyperglycaemia, insulin resistance, atherogenic dyslipidaemia, increased visceral fat and inflammation, are associated with increased risk in this population and represent potential targets for treatment. In this review, management strategies for patients with T2DM post-ACS, both in the acute-care setting and in the long-term, are discussed. Although the benefits of long-term, aggressive, multifactorial risk factor modification are well established, a significant burden of recurrent events remains and the search for novel strategies continues. Several studies are assessing the potential cardiovascular (CV) benefits and safety of various classes of newer agents. Of these, AleCardio (aleglitazar), Examination of Cardiovascular Outcomes With Alogliptin versus Standard of Care in Patients With Type 2 Diabetes Mellitus and Acute Coronary Syndrome (EXAMINE; alogliptin) and Evaluation of LIXisenatide in Acute Coronary Syndrome (ELIXA; lixisenatide) specifically address patients with type 2 diabetes post-ACS. The mechanisms of action of these new therapies and aims of the CV outcome studies are briefly reviewed. The prevalence of type 2 diabetes continues to increase worldwide highlighting the need for new strategies that address the complex underlying processes that drive atherosclerosis and CV events in this high-risk patient population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".