Increasing Societal Benefit From Cardiovascular Drugs
Bibliographic record
Abstract
During the past few years, several innovative treatments for noncommunicable chronic disease have become available, including SGLT2i (sodium-glucose cotransporter-2 inhibitors), GLP-1a (glucagon-like-peptide 1 agonists), ARNI (angiotensin receptor-neprilysin inhibitors), and finerenone, a selective nonsteroidal mineralocorticoid receptor antagonist. Each of these medications improves clinically relevant outcomes when added to existing therapies, and the indications for their use are rapidly expanding. Because existing drug regimens are already complex and costly, ensuring that society derives the maximal benefit from these new agents represents a major challenge. This Primer discusses how society can meet this challenge, which we address in terms of 5 principles: maximizing benefit, minimizing harm, optimizing uptake, increasing value for money, and ensuring equitable access. The Primer is most relevant for stakeholders in high-income countries, but the principles are broadly applicable to stakeholders in other settings, including low- and middle-income countries. We have focused the discussion on SGLT-2i, but the 5 principles herein could be used with reference to ARNI, finerenone, or any other health product.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".