Bibliographic record
Abstract
Endomyocardial biopsy (EMB) is an invaluable and underused diagnostic tool for myocardial disease. The primary indications are surveillance of cardiac allograft rejection and the diagnosis of inflammatory and infiltrative cardiomyopathies. EMB is typically performed by sampling the right ventricular septum via the right internal jugular vein using fluoroscopic guidance. The diagnostic yield of EMB is improved by sampling both ventricles and with the use of guidance from imaging or electroanatomic mapping. The risk of major cardiac complications is operator dependent and < 1% in experienced centres. EMB is the gold standard and most common form of cardiac allograft rejection surveillance, whereas advanced cardiac imaging and donor-specific antibody quantification provide complementary information. Gene expression profiling is an alternative surveillance strategy to EMB for low-risk patients. EMB is recommended for myocarditis and can guide therapy for giant-cell myocarditis, necrotizing eosinophilic myocarditis, sarcoidosis, and immune checkpoint inhibitor myocarditis. There is growing interest in using EMB to guide therapy for viral myocarditis, although the uptake of this approach is limited to specialized centres. EMB has been replaced as a first-line test for infiltrative cardiomyopathy by nonbiopsy diagnostic techniques, but is still useful to clarify the diagnosis or disease subtype. The miniaturization of bioptomes and advances in laboratory techniques such as microarrays promises to improve the safety and yield of EMB. We review the contemporary use of EMB for cardiac allograft rejection, inflammatory cardiomyopathy, and infiltrative cardiomyopathy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".