Cardiodiabetology: newer pharmacologic strategies for reducing cardiovascular disease risks
Bibliographic record
Abstract
Globally, nearly 500 million adults currently have diabetes, which is expected to increase to approximately 700 million by 2040. Cardiovascular diseases (CVD), including coronary heart disease, stroke, heart failure, and peripheral arterial disease, are the principal causes of death in persons with diabetes. Key to the prevention of CVD is optimization of associated risk factors. However, few persons with diabetes are at recommended targets for key CVD risk factors including low-density lipoprotein-cholesterol (LDL-C), blood pressure, glycated hemoglobin, nonsmoking status, and body mass index. While lifestyle management forms the basis for the prevention and control of these risk factors, newer and existing pharmacologic approaches are available to optimize the potential for CVD risk reduction, particularly for the management of lipids, blood pressure, and blood glucose. For higher-risk patients, antiplatelet therapy is recommended. Medication for blood pressure, statins, and most recently, icosapent ethyl, have evidence for reducing CVD events in persons with diabetes. Newer medications for diabetes, including sodium glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor agonists, also reduce CVD and SGLT2 inhibitors in particular also reduce progression of kidney disease and reduce heart failure hospitalizations (HFHs). Most importantly, a multidisciplinary team is required to address the polypharmaceutical options to best reduce CVD risks persons with diabetes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".