DETECTION OF LATENT CORONARY ARTERY DISEASE IN PATIENTS WITH ATRIAL FIBRILLATION AND HIGH CARDIOVASCULAR RISK: A CLINICAL ASPECT
Bibliographic record
Abstract
Goal. To identify the frequency and features of coronary artery disease in patients with AF without an established diagnosis of coronary artery disease who underwent coronary angiography for the first time. Materials and methods. The study included 31 patients with AF who were hospitalized in the cardiology department. All participants had not previously undergone invasive coronary artery imaging and had no history of coronary artery disease. All patients underwent coronary angiography. In addition, a Framingham cardiovascular risk assessment was performed, clinical and demographic data were collected, including age, the presence of risk factors (hypertension, hypercholesterolemia, smoking, diabetes mellitus), symptoms corresponding to angina pectoris, and concomitant pathology. Results. Coronary artery disease was detected in 11 patients (35.5%) out of 31. At the same time, 8 patients (25.8%) required myocardial revascularization — percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). The presence of coronary heart disease was associated with older age, hypercholesterolemia, a history of angina pectoris, and the presence of risk factors such as smoking. The data obtained indicate that in patients with AF and high cardiovascular risk, the presence of coronary artery disease may be underestimated in the absence of typical symptoms. Conclusions. Conducting coronary angiography in patients with atrial fibrillation, especially in the presence of high cardiovascular risk, seems clinically justified. This allows timely detection of latent coronary heart disease and necessary treatment, including revascularization, thereby reducing the risk of adverse cardiovascular events and improving the prognosis of this category of patients.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".