The effectiveness of national guidelines for preventing cardiovascular disease: integrating effectiveness concepts and evaluating guidelines' use in the real world
Bibliographic record
Abstract
PURPOSE OF REVIEW: Clinical guidelines historically have emphasized efficacy (intervention benefit in a controlled or experimental setting) rather than effectiveness (intervention benefit in the real world). This review examines how concepts of effectiveness currently influence the development of guidelines for prevention of cardiovascular disease, and how these concepts contribute to increased evaluation of guideline effectiveness. RECENT FINDINGS: A growing emphasis on effectiveness in cardiovascular preventive guidelines is largely influenced by a desire for guidelines to facilitate improved quality of care. Single risk factor guidelines for cardiovascular disease are being merged, and guideline recommendations are becoming simpler and easier to implement into practice. Simultaneously, guideline programs are undertaking a wider range of development, implementation and evaluation activities. Implementation science, a supporting discipline, is rapidly maturing and resulting in increasingly sophisticated studies designed to test the effectiveness of prevention interventions and approaches to improve guideline uptake. Modelling studies are used to develop effective guideline recommendations and evaluate the overall effectiveness of guidelines for reducing cardiovascular disease. SUMMARY: A sea change is occurring in cardiovascular guidelines toward effectiveness and prevention of multiple chronic diseases, and away from a focus on the efficacy of medical therapy for individual cardiovascular disease risk factors. The 'effectiveness sea' should eventually stretch to embrace cost-effectiveness and population effectiveness.
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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 | 0.057 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".