Managing thrombotic risk in patients with diabetes
Bibliographic record
Abstract
It is well known that diabetes is a prominent risk factor for cardiovascular (CV) events. The level of CV risk depends on the type and duration of diabetes, age and additional co-morbidities. Diabetes is an independent risk factor for atrial fibrillation (AF) and is frequently observed in patients with AF, which further increases their risk of stroke associated with this cardiac arrhythmia. Nearly one third of patients with diabetes globally have CV disease (CVD). Additionally, co-morbid AF and coronary artery disease are more frequently observed in patients with diabetes than the general population, further increasing the already high CV risk of these patients. To protect against thromboembolic events in patients with diabetes and AF or established CVD, guidelines recommend optimal CV risk factor control, including oral anticoagulation treatment. However, patients with diabetes exist in a prothrombotic and inflammatory state. Greater clinical benefit may therefore be seen with the use of stronger antithrombotic agents or innovative drug combinations in high-risk patients with diabetes, such as those who have concomitant AF or established CVD. In this review, we discuss CV risk management strategies in patients with diabetes and concomitant vascular disease, stroke prevention regimens in patients with diabetes and AF and how worsening renal function in these patients may complicate these approaches. Accumulating evidence from clinical trials and real-world evidence show a benefit to the administration of non-vitamin K antagonist oral anticoagulants for stroke prevention in patients with diabetes and AF.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".