State of the art: optimal medical therapy – competing with or complementary to revascularisation in patients with coronary artery disease?
Bibliographic record
Abstract
The role of coronary revascularisation with PCI and CABG in patients with stable and unstable coronary artery disease (CAD) is well established and there is a general consensus among guidelines as regards the indications for coronary revascularisation. Although revascularisation has undoubtedly revolutionised the treatment of CAD, it is vital to understand the recent advances and importance of the concomitant use of evidence-based optimal medical therapy (OMT). In contemporary practice, OMT should include an antiplatelet agent (or dual antiplatelet therapy when indicated) and a lipid-lowering drug for all patients, and a beta-blocker and an ACE inhibitor (or angiotensin receptor blocker) for the vast majority of patients, along with addressing cardiac risk factors and lifestyle management. OMT is the recommended initial choice for patients with stable angina pectoris, and the indication for revascularisation would be persistence of symptoms despite OMT and/or improvement of prognosis. For patients with acute coronary syndromes or those who underwent coronary revascularisation with either PCI or CABG, long-term use of OMT improves clinical outcomes and prognosis.
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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".