Prolonged dual antiplatelet therapy in renal failure: a challenging trade-off.
Bibliographic record
Abstract
Patients with renal failure and coronary artery disease (CAD) represent a complex and delicate cohort. Obviously, their management can be challenging and requires attention and expertise. Globally, the prevalence of chronic kidney disease (CKD), defined as an estimated glomerular filtration rate (eGFR) <60 mL/min per 1.73 m 2 and/or the presence of albuminuria, seems to constantly increase with CKD now having evolved as a global public health issue (1,2). Nowadays, the most important factors promoting the development of CKD worldwide include aging, obesity, diabetes, hypertension and atherosclerotic disease (1-3). Renal failure is not only the consequence of manifold systemic diseases, but also has systemic adverse effects and is related to high morbidity and mortality, even at an early stage (4,5). Among patients with established cardiovascular disease, CKD is related to a higher rate of adverse events, including atherothrombotic manifestations and hemorrhagic events (3). Taken together, those factors contribute to the massive increase in mortality in patients with both CAD and CKD (6).
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".