Abstract 086: Secondary Prevention of Cardiovascular Disease in Patients With Type 2 Diabetes: International Insights From the TECOS Trial
Bibliographic record
Abstract
Background: Intensive risk factor modification significantly improves outcomes for patients with diabetes and cardiovascular disease (CVD). However, the degree to which secondary prevention treatment targets are achieved in international clinical practice is unknown. Methods: Attainment of 5 secondary prevention targets—aspirin use, lipid control (low-density lipoprotein cholesterol (LDL-C) <70 mg/dL or statin therapy), blood pressure control (<140 mmHg systolic, <90 mmHg diastolic), angiotensin-converting enzyme inhibitor or angiotensin receptor blocker use, and non-smoking status—was evaluated among 14,671 patients from 38 countries with diabetes and known CVD at entry into TECOS. Logistic regression was used to evaluate the association between individual and regional factors and target achievement. Results: Overall, 29.9% of patients with diabetes and CVD had all 5 secondary prevention measures at target. North America had the highest proportion (41.2%), whereas Western Europe, Eastern Europe, and Latin America had proportions of approximately 25%. The likelihood of having individual prevention components at target also varied by region: compared with North America, individuals in all other regions were less likely to have blood pressure at goal, and individuals in Eastern Europe and Latin America were less likely to have LDL-C at target or to be on statin therapy (see Figure). Overall, blood pressure control (57.9%) had the lowest overall attainment while non-smoking status had the highest (89%). Conclusions: On a global scale, significant opportunities exist to improve the quality of cardiovascular secondary prevention care among patients with diabetes and CVD, which in turn could lead to reduced risk of downstream cardiovascular events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".