Physiological Implications of Myocardial Scar Structure
Bibliographic record
Abstract
ABSTRACT Once myocardium dies during a heart attack, it is replaced by scar tissue over the course of several weeks. The size, location, composition, structure, and mechanical properties of the healing scar are all critical determinants of the fate of patients who survive the initial infarction. While the central importance of scar structure in determining pump function and remodeling has long been recognized, it has proven remarkably difficult to design therapies that improve heart function or limit remodeling by modifying scar structure. Many exciting new therapies are under development, but predicting their long‐term effects requires a detailed understanding of how infarct scar forms, how its properties impact left ventricular function and remodeling, and how changes in scar structure and properties feed back to affect not only heart mechanics but also electrical conduction, reflex hemodynamic compensations, and the ongoing process of scar formation itself. In this article, we outline the scar formation process following a myocardial infarction, discuss interpretation of standard measures of heart function in the setting of a healing infarct, then present implications of infarct scar geometry and structure for both mechanical and electrical function of the heart and summarize experiences to date with therapeutic interventions that aim to modify scar geometry and structure. One important conclusion that emerges from the studies reviewed here is that computational modeling is an essential tool for integrating the wealth of information required to understand this complex system and predict the impact of novel therapies on scar healing, heart function, and remodeling following myocardial infarction. © 2015 American Physiological Society. Compr Physiol 5:1877‐1909, 2015.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".