Family Coronary Heart Disease: A Call to Action
Bibliographic record
Abstract
Abstract A family history of coronary heart disease (CHD) is an accepted risk factor for cardiovascular events and is independent of common CHD risk factors. Advances in the understanding of genetic influences on CHD risk provide the opportunity to apply this knowledge and improve patient care. Utility of inherited cardiovascular risk testing exists by utilizing both phenotypes and genotypes and includes improved CHD risk prediction, selection of the most appropriate treatment, prediction of outcome, and family counseling. The major impediment to widespread clinical adoption of this concept involves un‐reimbursed staff time, educational needs, access to a standardized and efficient assessment mechanism, and privacy issues. The link between CHD and inheritance is indisputable and the evidence strong and consistent. For clinicians, the question is how to utilize this information, in an efficient manner, in order to improve patient care and detection of high‐risk family members. Copyright © 2010 Wiley Periodicals, Inc. Robert Superko has no conflicts. Lakshmana Pendyala has no conflicts. Brenda Garrett is a consultant for CardioDx. Spencer King has no conflicts. Supported in part by FEMA grant No. 2006‐FP‐01744 and the Cholesterol, Genetics and Heart Disease Institute.
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 | 0.034 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.039 | 0.046 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".