Ventricular Remodeling Does Not Accompany the Development of Heart Failure in Diabetic Patients After Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Diabetic patients are at increased risk for heart failure (HF) and other adverse events after myocardial infarction (MI). Left ventricular (LV) enlargement after MI is also associated with the same increased risk. We used data from the Survival and Ventricular Enlargement (SAVE) echocardiographic substudy to test the hypothesis that diabetes was associated with increased LV enlargement after MI. METHODS AND RESULTS: Four hundred twelve nondiabetic and 100 diabetic patients underwent echocardiographic assessment at baseline and 3 months, 1 year, and 2 years after MI. HF developed in 30% of diabetic and 17% of nondiabetic patients during follow-up (P<0.001). Baseline LV diastolic size, ejection fraction, and infarct segment length were similar between diabetic and nondiabetic patients. Diabetic patients demonstrated less LV enlargement between baseline and 2 years than nondiabetic patients (0.9+/-11.1 cm2 versus 3.8+/-10.9 cm2, P=0.047). In patients who developed HF, LV diastolic dilatation (10.0+/-12.4 cm2 versus 3.7+/-13.1 cm2, P=0.06) and systolic dilatation (4.6+/-11.8 versus 0.91+/-12.1, P=0.017) were greater in nondiabetic than in diabetic patients. LV dilatation between baseline and 2 years was a predictor of HF in nondiabetic patients, but not in diabetic patients, even after excluding patients with recurrent MI and adjusting for history of hypertension, prior MI, age, treatment group, and smoking. Diabetes modified the relationship between ventricular enlargement and the risk of HF (P=0.011). CONCLUSIONS: The increased incidence of HF after MI in diabetic patients is not explained by a greater propensity for LV remodeling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".