Myocardial injury following transcatheter aortic valve implantation: insights from delayed-enhancement cardiovascular magnetic resonance
Bibliographic record
Abstract
AIMS: The aim of this study was to evaluate the presence, localisation and extent of myocardial injury as determined by late gadolinium enhancement (LGE) on cardiovascular magnetic resonance (CMR) imaging in patients undergoing transcatheter aortic valve implantation (TAVI). METHODS AND RESULTS: A total of 37 patients, who underwent successful TAVI with a balloon-expandable valve (transapical [TA], n=11; non-TA, n=26), were included. Cardiac biomarker (CK-MB and cTnT) levels were determined at baseline and following TAVI. CMR was performed within a week before and within 30 days following TAVI. Some increase in cardiac biomarkers was detected in 97% of the patients as determined by a rise in cTnT, and in 49% of the patients as determined by a rise in CK-MB. Following TAVI, no new myocardial necrosis defects were observed with the non-TA approach. Nonetheless, all patients who underwent TAVI through the TA approach had new focal myocardial necrosis in the apex, with a median myocardial extent and necrotic mass of 5% [2.0-7.0] and 3.5 g [2.3-4.5], respectively. CONCLUSIONS: Although some increase in cardiac biomarkers of myocardial injury was systematically detected following TAVI, new myocardial necrosis as evaluated by CMR was observed only in patients undergoing the procedure through the TA approach, involving ~5% of the myocardium in the apex.
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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".