Can the 2019 AHA/ACC Cardiovascular Guidelines Be Improved?
Bibliographic record
Abstract
The 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease for treatment of atherosclerosis in asymptomatic individuals is an advance over previously published recommendations. However, since all guidelines are based on a limited quantity of published studies and result from a consensus of experts in different fields of medicine, omissions and errors are inevitable. The present manuscript identifies four areas in these 2019 AHA/ACC guidelines that alternative approaches or changes would result in improved outcomes and reductions in atherosclerotic events. First, the goal for the reduction in the prevalence of cardiovascular disease should be total (100%) eradication of the disease. This is a feasible goal as the facilities and resources to accomplish this task are currently available. Second, guidelines should acknowledge that atherosclerosis is a reversible disease as has previously been documented by multiple studies. If reversible, then under the appropriate clinical circumstances, it is preventable. Third, the goal for LDL cholesterol reduction should be <50 mg/dl, if eradication of atherosclerosis is to be achieved. This goal is achievable and safe as suggested by published studies. Fourth, widespread use of the coronary artery calcium scan needs to be recommended so that early atherosclerosis can be reversed before a major cardiovascular event occurs. Treating all individuals at a specific risk category without regard for the presence of disease results in poor adherence to therapy and unnecessary side effects. Consideration of these four issues would improve the AHA/ACC guidelines and result in better patient care.
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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 | 0.047 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.022 | 0.022 |
| Insufficient payload (model declined to judge) | 0.023 | 0.023 |
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".