Newly Diagnosed and Previously Known Diabetes Mellitus and 1-Year Outcomes of Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: A prior diagnosis of diabetes mellitus is associated with adverse outcomes after acute myocardial infarction (MI), but the risk associated with a new diagnosis of diabetes in this setting has not been well defined. METHODS AND RESULTS: We assessed the risk of death and major cardiovascular events associated with previously known and newly diagnosed diabetes by studying 14,703 patients with acute MI enrolled in the VALsartan In Acute myocardial iNfarcTion (VALIANT) trial. Patients were grouped by diabetic status: previously known diabetes (insulin use or diagnosis of diabetes before MI, n=3400, 23%); newly diagnosed diabetes (use of diabetic therapy or diabetes diagnosed at randomization [median 4.9 d after infarction], but no known diabetes at presentation, n=580, 4%); or no diabetes (n=10,719). Patients with newly diagnosed diabetes were younger and had fewer comorbid conditions than did patients with previously known diabetes. At 1 year after enrollment, patients with previously known and newly diagnosed diabetes had similarly increased adjusted risks of mortality (hazard ratio [HR] 1.43; 95% confidence interval [CI], 1.29 to 1.59 and HR, 1.50; 95% CI, 1.21 to 1.85, respectively) and cardiovascular events (HR, 1.37; 95% CI, 1.27 to 1.48 and HR, 1.34; 95% CI, 1.14 to 1.56). CONCLUSIONS: Diabetes mellitus, whether newly diagnosed or previously known, is associated with poorer long-term outcomes after MI in high-risk patients. The poor prognosis of patients with newly diagnosed diabetes, despite having baseline characteristics similar to those of patients without diabetes, supports the idea that metabolic abnormalities contribute to their adverse outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".