Bibliographic record
Abstract
The goals of therapy for unstable angina and non-Q-wave myocardial infarction (MI) are to maintain myocardial perfusion by inhibiting platelet aggregation and fibrin deposition at sites of plaque rupture, thereby preventing ongoing or new myocardial ischemia and cardiac death. Although aspirin and heparin sodium are cornerstones in the management of unstable angina and non-Q-wave MI, both have significant limitations that have prompted the development of new agents. The thienopyridines, ticlopidine hydrochloride and clopidogrel, appear to be at least as effective as aspirin in the management of unstable angina. Glycoprotein IIb/IIIa receptor antagonists are a new class of platelet inhibitors that are more potent than aspirin, because they target the final common pathway of platelet aggregation. Low-molecular-weight heparins provide a more stable pharmacodynamic response and are more convenient to use than unfractionated heparin. Direct thrombin inhibitors show promise for inhibiting thrombin-mediated platelet aggregation and fibrin deposition. We focus on the opportunities presented by these agents, detailing mechanisms of action, advantages over aspirin and heparin, and performance in recent clinical trials.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".