Rejection Surveillance After Heart Transplantation: Is Paired Noninvasive Testing the New Gold Standard?
Bibliographic record
Abstract
Rejection surveillance after heart transplantation has traditionally relied on numerous endomyocardial biopsies, most of which occur during the first posttransplant year. With the introduction of gene expression profiling and, more recently, donor-derived cell-free DNA, a great proportion of surveillance is being performed noninvasively with both tests. Although patients have welcomed the use of paired testing because of the decreased risk and inconvenience, interpretation of both tests can sometimes be challenging, particularly when the test results are discordant. Growing evidence from both single-center experiences and large national databases has given insights that have allowed the field to operationalize dual testing and provide physicians with algorithms to approach paired testing. The increased use of noninvasive testing has also begun to challenge the role of biopsy as the gold standard for graft monitoring, not only for rejection but over the life of the heart transplant. In a growing number of circumstances, cell-free DNA not only may be a better means of assessing rejection but could also redefine how clinicians approach the diagnosis and even treatment of graft injury. As the heart transplant community garners more experience and generates more data, the current paradigms of heart transplant surveillance will continue to be challenged.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".