Influence of Diabetes Mellitus on development of Heart Failure following anterior Myocardial Infarction
Bibliographic record
Abstract
Background: Diabetic patients are at an increased risk of developing heart failure after myocardial infarction, particularly when left ventricular function is severely impaired.We aimed to investigate the influence of diabetes on the development of heart failure in a dynamically changing post-myocardial infarction population.Methods: In 272 patients enrolled in the Healing and Early Afterload Reducing Therapy (HEART) trial, left ventricular volumes, ejection fraction, and infarct segment length were assessed by echocardiography on day 1, day 14 and day 90.Echocardiographic measures, as well as clinical outcomes, were compared between diabetics and non-diabetics.Results: At presentation with myocardial infarction, diabetics (n = 56, 21%) demonstrated higher Killip class than non-diabetics (n = 216, 79%) despite similar infarct size.Changes in left ventricular size and function in the three months following infarction were similar in diabetics and non-diabetics.At one-year followup, diabetics demonstrated increased incidence of heart failure (29% versus 14%, p = 0.014) and total cardiovascular events (39% versus 19%, p = 0.002).Conclusions: The coexistence of diabetes enhances the risk for developing heart failure after myocardial infarction even in patients with mild left ventricular dysfunction or preserved ejection fraction.This risk is independent of infarct size, ejection fraction, or subsequent ventricular remodeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".