Bleeding and blood transfusion issues in patients with non-ST-segment elevation acute coronary syndromes
Bibliographic record
Abstract
Antithrombotic therapy and invasive risk stratification in selected high-risk patients have improved outcomes from non-ST-segment elevation acute coronary syndromes (NSTE-ACS), but carry a risk of bleeding and blood transfusion. Although the true incidence of bleeding depends on the population studied (i.e. clinical trial vs. registry), the definition used, and the use of invasive procedures, it is becoming clear that bleeding is associated with an increased risk for adverse outcomes including myocardial infarction and death. Blood transfusion itself may carry a risk for ischaemic outcomes that is independent of bleeding. Therefore, therapies that provide an adequate level of anticoagulation to reduce ischaemia while simultaneously minimizing the risk of bleeding and transfusion have the potential to improve outcomes among patients with NSTE-ACS. Anticoagulants studied in recent clinical trials that have focused on bleeding reduction include fondaparinux and bivalirudin. In this review, we discuss the clinical trial evidence for these agents, the association between bleeding and clinical outcomes, the biology of blood transfusion and potential mechanisms underlying its association with adverse outcomes, and propose strategies to deal with bleeding complications. Future directions for research and clinical practice are also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".