Cardiology: What You May Have Missed in 2023
Bibliographic record
Abstract
Cardiology and all its subspecialties continue to push the envelope in developing new treatment strategies for a wide variety of diseases. After screening more than 1300 articles, we highlight a selection of important cardiology articles published in 2023. Starting with prevention, we note articles that look at the effect of semaglutide in patients with obesity as well as a first-in-class drug, bempedoic acid, on cardiovascular outcomes. We have also examined new evidence comparing conservative management with invasive management of frail, older patients with non-ST-segment elevation myocardial infarction (NSTEMI). In patients with cardiac arrest secondary to NSTEMI, another article examines the rationale for expedited transfer to a cardiac arrest center. The STREAM-2 (Strategic Reperfusion in Elderly Patients Early After Myocardial Infarction) trial builds on looking at half-dose thrombolysis in older populations with STEMI. Emphasis is placed on guideline-directed medical therapy before hospital discharge in those with heart failure. In addition, in patients with stable symptomatic coronary artery disease, initial noninvasive testing using coronary computed tomography angiography may be a viable option compared with invasive strategies. More details have emerged on anticoagulation strategies in those with device-detected atrial fibrillation. Finally, transcatheter approaches to treat both mitral and tricuspid regurgitation have also been included.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.131 | 0.085 |
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".