Medical treatment, PCI, or CABG for coronary artery disease?
Bibliographic record
Abstract
The three approaches should complement one another, not compete Medical treatments of coronary artery disease have improved in the past decade because of the availability of statins, effective blood pressure lowering drugs (in particular angiotensin converting enzyme inhibitors), calcium blockers, and antiplatelet agents.1 In addition, improvements in percutaneous coronary interventions (PCI) have revolutionised the management of high risk people with acute myocardial infarction (primary PCI and rescue PCI) and non-ST elevation myocardial infarction and unstable angina. The use of stents, together with antiplatelet and antithrombotic treatments, has reduced procedural complications and made PCI safer.2 Drug eluting stents have reduced restenosis after PCI, although they increase late stent thrombosis, for which long term dual antiplatelet treatment is required.3 Improvements in coronary artery bypass (CABG) surgery have been slow because only a few randomised controlled trials have been performed. Surgeons still debate the benefits of off-pump CABG (beating heart surgery) versus on-pump surgery,4 and whether double internal mammary artery grafts are superior to single internal mammary grafting. Both questions are currently being evaluated in large randomised trials.5 Given that CABG surgery is increasingly performed in older people who are at high risk of cardiac, neurological, and renal complications, it is notable that CABG surgery results are improving worldwide. In October 2010, a joint task force of the European Society of …
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 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".