Assessment of significant coronary artery stenosis using blood oxygen level dependent cardiovascular magnetic resonance (BOLD-CMR)
Bibliographic record
Abstract
Using the magnetic properties of hemoglobin, changes in myocardial tissue oxygenation can be detected with blood oxygen level dependent (BOLD) cardiovascular MRI (CMR). The study aim was to assess whether BOLD-CMR images can detect an abnormal myocardial tissue response to adenosine infusion in patients with CAD, when compared to fractional flow reserve (FFR). Patients undergoing clinically indicated coronary angiography underwent BOLD CMR scans using a clinical 1.5T scanner. Three short axis BOLD cine images were captured at baseline and during adenosine-induced coronary hyperemia. The mean segmental percent signal intensity (SI) changes were calculated between baseline and hyperemia in the subendocardial myocardium using the 16-segment model. Segments were defined as ischemic or non-ischemic by FFR (cut-off <0.80). The segment with the lowest BOLD SI percent change per patient was used for analysis. Thirty-two patients were enrolled, 5 patients were excluded due to incomplete CMR, leaving 27 patients (age 61 ± 10 years) for analysis. There were 864 myocardial segments (baseline and adenosine) available for analysis, 289 were subtended by a coronary artery with an available FFR value. Eighty-two segments (28%) were excluded due to pre-defined criteria for poor image quality, 67% were apical. From the remaining 20 patients, 7 had ischemic FFR values and 13 had non ischemic FFR values. Using the segment with the lowest % BOLD SI change per patient there was a significant difference between ischemic -6.49% ± -8.65% and non ischemic 4.21 ± 4.94% (p=0.0023) patients. Using a cut off value of 1.1% SI change the sensitivity is 86%, specificity 69%, positive predictive value 0.6 and negative predictive value 0.9. A blunted hyperemic response to adenosine as assessed by oxygenation-sensitive CMR at 1.5T can identify functionally significant coronary artery stenosis. However, image quality, mainly in apical segments, remains a limitation.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".