The risk of iatrogenic bleeding in acute coronary syndromes and long-term mortality
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review examines the association between bleeding and adverse outcomes in patients with acute coronary syndrome and explores mechanisms behind this association and strategies for reducing bleeding complications in acute coronary syndrome. RECENT FINDINGS: Bleeding is a common complication of antithrombotic treatment in acute coronary syndrome, and major bleeding occurs in around 5% of patients. Important risk factors for major bleeding include increasing age, female sex, renal impairment, and invasive procedures. Recent studies suggest that major bleeding in patients with acute coronary syndrome is independently associated with an increase of early and long-term morbidity and mortality. This may be due to the direct effects of anaemia and hypovolaemia, the treatment modification or withdrawal, or the adverse effects of transfusion. Bleeding complications may be reduced by use of new antithrombotic agents and by improved attention to dosing with current agents. SUMMARY: Future studies should examine the effects on overall morbidity and mortality of strategies designed to reduce bleeding complications in patients with acute coronary syndrome. There is a need to apply uniform definitions of bleeding severity. Future trials should report all clinically relevant bleeding outcomes and transfusions. Studies are needed to investigate methods to reduce the risk of bleeding, better understand mechanisms of adverse outcome after bleeding, and establish best practice for the management of bleeding including appropriate use of transfusion in patients with acute coronary syndrome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".