The contribution of familial and heritable risks in heart failure
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to summarize the recent literature regarding the familial heritability of heart failure and to discuss the possible mechanisms through which this risk is mediated. RECENT FINDINGS: Data from the Framingham Heart Study recently showed that the parental occurrence of heart failure increases the risk of heart failure in offspring. Although the mechanisms mediating this increased risk are not elucidated, heritable risks of heart failure may result from genes affecting the cardiac or vascular systems. Alternatively, familial risk may be mediated partly through the inheritance of recognized or as yet unidentified risk factors for heart failure. Heritable components or genetic loci for quantitative traits contribute to the development of hypertension, coronary artery disease, cardiomyopathies, valvular heart disease, and metabolic conditions, which collectively increase the risk of heart failure. SUMMARY: A careful assessment of the family history of heart failure and associated risk factors may identify treatable targets that can potentially reduce the likelihood of developing heart failure, and can assist in the implementation of preventive strategies for risk populations with stages A and B heart failure.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".