Treatment of Stable Angina: Medical and Invasive Therapy—Implications for the Elderly
Bibliographic record
Abstract
Currently available therapies for chronic stable angina are reviewed. Revascularization, i.e., coronary artery bypass surgery and percutaneous transluminal coronary angioplasty, is summarized briefly, with short- and long-term results summarized from several large registries and review articles. Advancing age is a risk factor for both coronary artery bypass surgery and percutaneous transluminal coronary angioplasty, but risks of coronary events are also higher without interventions in the elderly. In-hospital mortality for coronary artery bypass surgery is about 8% for patients over age 80 in one large national registry and not much different in elective coronary artery bypass surgery in highly-selected patients over age 90 in one institution. The few randomized trials of invasive vs. noninvasive therapy for stable coronary artery disease are described. Although patient numbers in available studies are too small to be conclusive as to which type of therapy is generally better, data appear to suggest that higher-risk patients have better outcomes with revascularization. Methods of risk stratification are discussed. Finally, unusual therapies for angina are briefly noted, including transmyocardial revascularization, external counterpulsation, and gene therapy.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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) | 0.002 | 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".