Preclinical Studies on the Effects of Frailty in the Aging Heart
Bibliographic record
Abstract
Age is a major risk factor for the development of cardiovascular diseases in men and in women. However, not all people age at the same rate and those who are aging rapidly are considered frail, compared with their fit counterparts. Frailty is an important clinical challenge because those who are frail are more likely to develop and die from illnesses, including cardiovascular diseases, than fit people of the same age. This increase in susceptibility to cardiovascular diseases in older individuals might occur as the cellular and molecular mechanisms involved in the aging process facilitate structural and functional damage in the heart. Consistent with this, recent studies in murine frailty models have provided strong evidence that maladaptive cardiac remodelling in older mice is the most pronounced in mice with a high level of frailty. For example, there is evidence that ventricular hypertrophy and contractile dysfunction increase as frailty increases in aging mice. Additionally, fibrosis and slowing of conduction in the sinoatrial node and atria are proportional to the level of frailty. These modifications could predispose frail older adults to diseases like heart failure and atrial fibrillation. This preclinical work also raises the possibility that emerging interventions designed to "treat frailty" might also treat or prevent cardiovascular diseases. These findings might help to explain why frail older people are most likely to develop these disorders as they age.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".