Coronary CT Angiography to Guide Percutaneous Coronary Intervention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Coronary CT angiography (CCTA) has emerged as a powerful noninvasive tool for characterizing the presence, extent, and severity of coronary artery disease (CAD) in patients with stable angina. Recent technological advancements in CT scanner hardware and software have augmented the rich information that can be derived from a single CCTA study. Beyond merely identifying the presence of CAD and assessing stenosis severity, CCTA now allows for the identification and characterization of plaques, lesion length, and fluoroscopic angle optimization, as well as enables the assessment of the physiologic extent of stenosis through CT-derived fractional flow reserve, and may even allow for the prediction of the response to revascularization. These and other features make CCTA capable of not only guiding invasive coronary angiography referral, but also give it the unique ability to help plan coronary intervention. This review summarizes current and future applications of CCTA in procedural planning for percutaneous coronary intervention, provides rationale for wider integration of CCTA in the workflow of the interventional cardiologist, and details how CCTA may help improve patient care and clinical outcomes. Keywords: CT Angiography © RSNA, 2022
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 it