Bleeding Complications in Patients with Acute Coronary Syndromes: Are They Important and How Can We Prevent Them?
Bibliographic record
Abstract
Progress in antithrombotic medications and revascularization procedures has helped to reduce mortality in patients with acute coronary syndromes (ACS). However, these therapies can significantly increase the risk of bleeding. Bleeding complications are important clinical outcomes in patients with ACS. Data from numerous randomized controlled trials and large registries have demonstrated that bleeding is independently associated with a significantly higher risk of mortality in patients with ACS. Furthermore, bleeding complications are associated with an increased risk of recurrent ischemic events. The challenge of preventing bleeding complications while obtaining the optimal antithrombotic benefits of ACS management requires careful consideration of factors associated with patients, pharmacotherapy, and interventional procedures. Clinicians should be able to risk stratify patients for bleeding events just as they would do for recurrent ischemic events in ACS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".