Bibliographic record
Abstract
It has been my pleasure to serve as President of the American Society for Preventive Cardiology (ASPC) over the past 2 years. During my tenure, we have revitalized our Society and strengthened our relationship with our journal, Preventive Cardiology. In November 2009 we held a joint symposium with the National Lipid Association prior to the American Heart Association’s Annual Meeting in Orlando, Florida. This symposium on “Overcoming Challenges in Cardiovascular Disease (CVD) Prevention: A Focus on the Cardiometabolic Risk Continuum,” enabled our Society to contribute our own special expertise to the proceedings, while strengthening our relationship with a partner society. My section, on treatment of hypertension based on global risk, was directly targeted at a key element of prevention and an example of how we convey our insights to the broader medical community. On March 5, 2010, we again reached out to partner organizations by participating in the Joint Conference–50th Cardiovascular Disease Epidemiology and Prevention and Nutrition, Physical Activity and Metabolism 2010, in San Francisco, California. We conducted a session on what has become a tradition for the ASPC, a debate on prevention guidelines that illustrated the range of opinion and depth of approach embraced by our field. We will continue to team up and partner with organizations that share our goals and mission and strengthen our Society while we enhance our branch of medicine. Note that there is an application for membership in the ASPC included in this journal and we invite you to apply for membership. We are looking forward to an active year in 2010, as health care is on the national agenda and prevention is in the spotlight and central to improving care and reducing costs and burden. By now our lawmakers are familiar with the grim statistics: over a third of adult Americans will experience coronary heart disease in their lifetimes. Whether the condition is angina pectoris, myocardial infarction, or coronary disease death, predicting the onset and occurrence of these events is critical in our clinical decision making. Going forward, practitioners should be thoroughly familiar with traditional approaches, as the multivariable prediction model developed by the Framingham investigators has been extensively validated with cohort studies and captures a range of factors, including age, sex, cholesterol level, and smoking status. Increasingly, we are seeing practitioners test the limits of our science. Physicians and other health care professionals are examining alternative measures of coronary heart disease and surrogate markers that may help to predict clinical events and guide preventive care in cardiology. Our task is to add to the body of knowledge and help researchers and clinicians alike gain greater insight into the process and theory of risk estimation. By gauging additional variables in models that include traditional risk assessment criteria, the statistical significance of alternate approaches may be clarified as to their predictive value. While there is room for improvement, our current standards for coronary heart disease risk assessment are proven to be relatively accurate. The value of new factors for assessment should be judged also for their applicability; traditional approaches are by now well understood and familiar to both clinicians and patients, and recommendations for medical therapy and lifestyle modification are practical and low in cost. Thus, while caution is warranted, we can also be cautiously optimistic that new developments now emerging from studies in omega 3 fatty acids and high-density lipoprotein cholesterol therapeutics will change and improve the landscape for diagnosis and prevention.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.386 | 0.312 |
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".