Aging and Remodeling During Healing of the Wounded Heart: Current Therapies and Novel Drug Targets
Bibliographic record
Abstract
Aging has become a major health care problem and socio-economic burden worldwide. Myocardial infarction (MI) is the major killer worldwide and coronary reperfusion is the major form of acute post-MI therapy. The aging population is increasing, and with it, morbidity and mortality due to impaired healing after ST-segment elevation MI (STEMI) and its consequences. Optimal healing of the wounded heart is critical for preservation of structural and functional integrity of the pumping chambers, survival, and a favorable outcome irrespective of age. Although STEMI is more prevalent in the elderly and impaired healing during aging may promote adverse remodeling and thereby jeopardize outcome, there is an information gap on post-STEMI healing and its therapy in the elderly. Current therapies during post-STEMI healing are aimed primarily at the <65 age-group and preclinical studies tend to test drugs in mostly young animals. Therapies over the last decade have improved post-MI survival mainly in patients aged < 65 years. Novel healing-specific proteins may provide potential targets for improving healing and limiting adverse remodeling of the post-STEMI heart in the elderly, thereby improving outcome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".