Cardiology: What You May Have Missed in 2024
Bibliographic record
Abstract
There have been many recent advancements in cardiology research, with numerous studies published across the multiple subspecialties. Having screened more than 1200 articles published in 2024, we summarize 10 studies in this article that highlight key changes in this field. Starting with atrial fibrillation (AF), we note articles that examine which patients benefit most from catheter ablation, a procedure that is becoming increasingly common. We then examine new evidence regarding anticoagulation in device-detected AF and in patients with AF and coronary disease. In patients with severe aortic stenosis, the timing of valve intervention in relation to development of symptoms was a hot topic and is addressed here. There have also been developments in treatment of heart failure with preserved ejection fraction, including research into medications such as finerenone and tirzepatide. Certain studies attempt to challenge our current medical practices, including routine use of β-blockers after myocardial infarction (MI) and holding of renin-angiotensin system inhibitors before noncardiac surgery. Finally, the role of invasive treatment strategies for older adults with non-ST-segment elevation MI has also been addressed.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.064 | 0.035 |
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".