Bibliographic record
Abstract
PURPOSE OF REVIEW: In spite of great scientific advances, cardiovascular disease is the commonest cause of death worldwide and current cardiovascular prevention strategies fail to achieve the full potential of risk modification. A large amount of evidence supports the use of pharmacological treatments both in primary and secondary prevention and it was hypothesized that a fixed-dose combination of such drugs, a 'polypill', may greatly simplify and improve current prevention strategies. RECENT FINDINGS: Several polypill formulations have been developed and a recent trial demonstrated the short-term feasibility, safety and efficacy (in reducing risk factor levels) of a polypill in individuals at moderate risk. Many challenges remain and studies are underway, which will address questions related to formulation of polypill(s), the long-term safety and tolerability, the efficacy in reducing risk factor levels and cardiovascular events, physician, patient and societal acceptability, adherence, regulatory requirements, cost and impact on lifestyle habits. SUMMARY: Theoretical models suggest that a polypill containing low-dose aspirin, three blood pressure-lowering drugs at half dose and a potent statin, administered to a large proportion of the population at risk for cardiovascular events, could reduce ischemic heart disease and strokes by over 80%. The feasibility of this approach has recently been shown in a clinical trial and ongoing studies will define whether the postulated benefits of the polypill will be observed.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".