New pharmacological agents and novel cardiovascular pharmacotherapy strategies in 2024
Bibliographic record
Abstract
Despite substantial advances in cardiovascular pharmacotherapy and devices in recent years, prevention and treatment of many cardiovascular diseases (CVDs) remain limited, thus reflecting the need for more effective and safer pharmacological strategies. In this review, we summarize the most relevant studies in cardiovascular pharmacotherapy in 2024, including the approval of first-in-class drugs for the treatment of resistant hypertension and pulmonary arterial hypertension, label expansions for bempedoic acid and semaglutide, and the results of major randomized clinical trials (RCTs) that have met the pre-specified primary endpoints, thereby filling some gaps in knowledge and opening new perspectives in the management of CVD, and those RCTs whose results did not confirm the proposed research hypotheses. We also include a section on drug safety, where we describe the newest data on adverse reactions and drug-drug interactions that may complicate treatment and/or reduce drug adherence with the consequent decrease in drug effectiveness. Finally, we present the most important ongoing phase 2 and phase 3 clinical trials assessing the efficacy and safety of cardiovascular drugs for the prevention and treatment 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".