Where Are We With Treatment and Prevention of Heart Failure in Patients Post–Myocardial Infarction?
Bibliographic record
Abstract
As a result of the widespread use of reperfusion therapies and secondary prevention over the last 30 years, there has been a dramatic reduction in the risk of mortality and development of heart failure (HF) following acute myocardial infarction (MI). Despite this, the development of chronic HF remains a common occurrence in the days, months, and years following MI. Neurohormonal inhibition remains the mainstay of pharmacologic prevention of HF following MI, with recent trials showing an additive benefit of a neprilysin inhibitor or a sodium glucose co-transporter 2 inhibitor in reducing the risk of development of HF but no significant effect on mortality. Novel imaging tools may help refine risk stratification in high-risk patients and allow greater targeting of preventative therapies in patients most likely to benefit. Research is ongoing into novel therapies aiming to minimize the degree of myocardial damage and prevention of progressive adverse remodeling following MI.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".