Comparison of X‐ray fluoroscopy and interventional magnetic resonance imaging for the assessment of coronary artery stenoses in swine
Bibliographic record
Abstract
The accuracy of a two-step interventional MRI protocol to quantify coronary artery disease was compared to the clinical gold standard, X-ray angiography. Studies were conducted in nine swine with a surgically induced stenosis in the proximal left circumflex coronary artery. The two-step protocol consisted of catheter-directed magnetic resonance angiography (MRA), which was first used to localize the stenosis, followed by MRI cross-sectional images to quantify the degree of stenosis without the use of contrast agent. Line signal intensity profiles were drawn across the vessel diameter at the stenosis site and proximal to the stenosis for each data set to measure percentage stenosis for each animal. Catheter-directed MRA successfully detected eight of nine stenoses. Cross-sectional MRI accurately quantified each stenosis, with strong agreement to the measurements made using X-ray fluoroscopy (intraclass correlation coefficient = 0.955; P < 0.05). This study demonstrates that in the future interventional MRI may be an alternative to X-ray angiography for the detection and quantification of coronary artery disease.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".