Right ventricular involvement in patients with coronary artery disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: RV involvement in coronary artery disease (CAD) includes isolated infarction and involvement in left ventricular infarction. Right ventricular involvement with left ventricular infarction has unique clinical signs and symptoms, requires different management, and has worse prognosis than left ventricular infarcts without right ventricular involvement. Although the right ventricle (RV) is geometrically complex, advances in echocardiography, nuclear imaging, computed tomography, and magnetic resonance imaging technologies have helped to optimally visualize its structure and function and to better elucidate its role in CAD. RECENT FINDINGS: Newer noninvasive imaging modalities to visualize the RV are highlighted and their emerging clinical utilities are emphasized, including three-dimensional echocardiography, tissue Doppler velocity and strain imaging, computed tomography, and MRI. SUMMARY: The RV is often involved in CAD. Available imaging modalities demonstrate different aspects of right ventricular involvement, yielding new insights into pathophysiology, clinical care, and management. As imaging technologies widen in their scope, cardiologists will increasingly have the imaging tools to integrate information on right ventricular morphology, hemodynamics, and function, enabling appropriate care for patients with right ventricular involvement in CAD.
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) | 0.004 | 0.003 |
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".