Stress cardiac MRI in stable coronary artery disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Non-invasive testing is often the first step in the evaluation of stable coronary artery disease (CAD). Stress cardiac magnetic resonance imaging (CMR) is an established modality with high diagnostic accuracy and prognostic value. This review will focus on the recent advances in understanding how stress CMR can help guide patient care. RECENT FINDINGS: Diagnostic accuracy of stress CMR has been validated against coronary angiography with fractional flow reserve (FFR) in patients with stable CAD. Large registry data have shown stress CMR to have important prognostic importance and that its cost-effectiveness compares favorably to alternatives. In patients with stable CAD, guidance using a CMR based strategy led to equivalent outcomes when compared to coronary angiography with FFR. SUMMARY: In persons with stable CAD, Stress CMR is an accurate and cost-effective imaging modality that should be considered in patients at intermediate pre-test probability of CAD. Prognostic studies have shown it to have excellent negative predictive value and that it can safely serve as a "gatekeeper" for invasive angiography.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".