Invasive and antiplatelet treatment of patients with non‐ST‐segment elevation myocardial infarction: Understanding and addressing the global risk‐treatment paradox
Bibliographic record
Abstract
Clinical guidelines for the treatment of patients with non-ST-segment elevation myocardial infarction (NSTEMI) recommend an invasive strategy with cardiac catheterization, revascularization when clinically appropriate, and initiation of dual antiplatelet therapy regardless of whether the patient receives revascularization. However, although patients with NSTEMI have a higher long-term mortality risk than patients with ST-segment elevation myocardial infarction (STEMI), they are often treated less aggressively; with those who have the highest ischemic risk often receiving the least aggressive treatment (the "treatment-risk paradox"). Here, using evidence gathered from across the world, we examine some reasons behind the suboptimal treatment of patients with NSTEMI, and recommend approaches to address this issue in order to improve the standard of healthcare for this group of patients. The challenges for the treatment of patients with NSTEMI can be categorized into four "P" factors that contribute to poor clinical outcomes: patient characteristics being heterogeneous; physicians underestimating the high ischemic risk compared with bleeding risk; procedure availability; and policy within the healthcare system. To address these challenges, potential approaches include: developing guidelines and protocols that incorporate rigorous definitions of NSTEMI; risk assessment and integrated quality assessment measures; providing education to physicians on the management of long-term cardiovascular risk in patients with NSTEMI; and making stents and antiplatelet therapies more accessible to patients.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".