Cardiovascular disease on a global scale: defining the path forward for research and practice
Bibliographic record
Abstract
During the 2006 World Congress of Cardiology meeting in Barcelona, the Virtual Coordinating Centre for Global Collaborative Cardiovascular Research (VIGOUR) group held a symposium examining potential approaches to understanding and controlling the explosive worldwide growth of cardiovascular disease and its attendant morbidity and mortality. Over the last 20 years, the global nature of many problems in health care has become much more evident. In the realm of health, this has meant that countries across the globe have started to experience the same kinds of behavioural shifts (overeating, reduced physical activity and smoking), and with them massive increases in cardiovascular risk factors, observed over the last century particularly in North America and Western Europe. This VIGOUR symposium focused on what actions can be taken now to prepare for this future in which prevention and treatment of cardiovascular disease will be a major public health issue in a much larger proportion of the world's countries. The participants focused on four major areas where they saw important opportunities: (i) the development of high quality, contemporaneous data sources that can be used to study and improve the processes, treatments and outcomes of cardiovascular diseases globally; (ii) the feasibility and resource/health economic implications of any proposed potential solutions need to be carefully defined; (iii) models/systems must be identified that can be used to guide effective interventions targeting health problems of large populations at an affordable price; (iv) academic research organizations need to assume a more active role in the health-care system both through their traditional activities in discovery research and developing evidence-based medicine along with translation of research findings into effective interventions that improve the public health.
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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 | 0.210 | 0.128 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.043 | 0.071 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.038 | 0.059 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".