Computed tomographic coronary angiography for patients with heart failure (CTA-HF): a randomized controlled trial (IMAGE HF Project 1-C)
Bibliographic record
Abstract
BACKGROUND: The prevalence of heart failure (HF) is rising in industrialized and developing countries. Though invasive coronary angiography (ICA) remains the gold standard for anatomical assessment of coronary artery disease in HF patients, alternatives are being sought. Computed tomographic coronary angiography (CTA) has emerged as an accurate non-invasive diagnostic tool for coronary artery disease (CAD) and has been demonstrated to have prognostic value. Whether or not CTA can be used in HF patients is unknown. Acknowledging the aging population, the growing prevalence of HF and the increasing financial burden of healthcare, we need to identify non-invasive diagnostic tests that are available, safe, accurate and cost-effective. METHODS/DESIGN: The proposed study aims to provide insight into the efficacy of CTA in HF patients. A multicenter randomized controlled trial will enroll 250 HF patients requiring coronary anatomical definition. Enrolled patients will be randomized to either CTA or ICA (n = 125 per group) as the first test to define coronary anatomy. The primary outcomes will be collected to determine downstream resource utilization. Secondary outcomes will include the composite clinical events and major adverse cardiac events. In addition, the accuracy of CTA for detecting coronary anatomy and obstruction will be assessed in patients who subsequently undergo both CTA and ICA. It is expected that CTA will be a more cost-effective strategy for diagnosis: yielding similar outcomes with fewer procedural risks and improved resource utilization. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01283659. Team grant #CIF 99470.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".