Use of Magnetic Resonance Imaging to Assess Myocardial Perfusion after Transmyocardial Laser Revascularization
Bibliographic record
Abstract
BACKGROUND: Transmyocardial laser revascularization (TMLR) is an alternative treatment modality for patients with refractory angina who are not candidates for conventional surgical or percutaneous revascularization. Clinical studies of TMLR have not shown one-to-one correlation between increased myocardial perfusion and improved clinical status. METHODS: Three patients (51, 53, and 70 years old) with severe, diffuse coronary artery disease not amenable to conventional surgical revascularization and with angina (Canadian Cardiovascular Society [CCS] class 3-4) refractory to maximal medical therapy underwent TMLR with a CO2 laser. Preoperative and postoperative cardiac magnetic resonance imaging (MRI) were performed to assess left ventricular perfusion and wall-motion changes in the laser-treated areas. Postoperative MRIs were performed within 6 months of TMLR and at 12 months. Angina status was assessed with the Seattle Angina Questionnaire. RESULTS: Postoperative adenosine stress myocardial perfusion imaging with MRI revealed improved overall perfusion and a reduction in subendocardial hypoperfused areas when compared to preoperative images. In all patients, an improvement in CCS class was consistent with an improvement in perfusion. CONCLUSIONS: Cardiac MRI can be used to assess improved subendocardial perfusion after TMLR treatment. In our study, we found that endocardial perfusion was maintained over a 12-month period.
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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 | 0.000 | 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".