Bibliographic record
Abstract
Acute coronary syndromes usually result from thrombotic occlusion of a coronary artery at the site of atherosclerotic plaque disruption. The mainstay of treatment is the use of antiplatelet and antithrombotic therapy to maintain patency of the artery. In patients with non-ST segment elevation acute coronary syndromes, antithrombotic therapy followed by coronary revascularization (when feasible in patients with high-risk features) is the optimal management strategy. In the patient with ST elevation acute coronary syndromes who receives a fibrinolytic agent antithrombotic agents, are also important to prevent reocclusion. Bleeding complications of antithrombotic therapy are associated with a substantial increase in adverse short- and long-term outcomes. Hence, the selection of the most appropriate antithrombotic agent aims to minimize both ischemic and hemorrhagic complications. Factors that are associated with increased bleeding risk and need to be considered when selecting an antithrombotic agent include decreased renal function, short time to invasive procedure (<24 h), and the overall bleeding risk. For patients who will undergo later cardiac catheterization and are not at high bleeding risk, either enoxaparin or fondaparinux are acceptable choices. For patients who are likely to undergo early catheterization or have an increased bleeding risk, either fondaparinux or unfractionated heparin are the optimal choice. Patients with severe impairment of renal function should receive unfractionated heparin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".