The evolving use of biomarkers in heart transplantation: Consensus of an expert panel
Bibliographic record
Abstract
In heart transplantation, the use of biomarkers to detect the risk of rejection has been evolving. In this setting, it is becoming less clear as to what is the most reliable test or combination of tests to detect rejection and assess the state of the alloimmune response. Therefore, a virtual expert panel was organized in heart and kidney transplantation to evaluate emerging diagnostics and how they may be best utilized to monitor and manage transplant patients. This manuscript covers the heart content of the conference and is a work product of the American Society of Transplantation's Thoracic and Critical Care Community of Practice. This paper reviews currently available and emerging diagnostic assays and defines the unmet needs for biomarkers in heart transplantation. Highlights of the in-depth discussions among conference participants that led to development of consensus statements are included. This conference should serve as a platform to further build consensus within the heart transplant community regarding the optimal framework to implement biomarkers into management protocols and to improve biomarker development, validation and clinical utility. Ultimately, these biomarkers and novel diagnostics should improve outcomes and optimize quality of life for our transplant patients.
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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 | 0.042 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| 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".