Why are we failing to implement effective therapies in cardiovascular disease?
Bibliographic record
Abstract
Worldwide, there are ~18 million deaths each year from cardiovascular disease and at least 2-3 times as many experience non-fatal cardiovascular events. Numerous evidence-based prevention and management guideline recommendations for cardiovascular disease are available. However, significant gaps between the evidence and its implementation persist ('evidence-practice gap'). There exist 'under-use' gaps with lack of implementation of proven effective strategies and 'over-use' gaps with inappropriate use of strategies with strong evidence against, or insufficient evidence for their effectiveness and safety. To better tackle the global burden of cardiovascular disease (CVD), more effective strategies are needed. We discuss three selected areas where advances in implementation research for CVD could provide improvements. First, a better assessment and understanding of the most important modifiable context-specific barriers to evidence-based care will allow optimal tailoring of interventions to overcome them. Second, novel community intervention strategies from outside current CVD research should be considered, especially for CVD areas where major barriers exist and little progress has been made. Examples of such interventions include cell phone text messaging, non-physician health workers for the delivery community CVD care in areas of need, and low-cost single-pill combination CVD therapy. Third, increasing our understanding of successful implementation and sustainability of improvements is essential for CVD as a widespread chronic disease. Learning how to better implement effective therapies for CVD will have a larger effect on patient outcomes than most single new drugs and is a priority for tackling the global burden of CVD.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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 teacher head, 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".