Review of interactions between high‐risk pediatric heart transplant recipients and marginal donors including utilization of risk score models
Bibliographic record
Abstract
BACKGROUND: Donor organ acceptance practices vary among pediatric heart transplant professionals. We sought to understand what is known about the interactions between the "high-risk" recipient and the "marginal donor," and how donor risk scores can impact this discussion. METHODS: A systematic review of published literature on pediatric HTx was undertaken with the assistance of a medical librarian. Two authors independently assessed search results, and papers were reviewed for inclusion. RESULTS: We found that there are a large number of individual factors, and clusters of factors, that have been used to label individual recipients "high-risk" and individual donors "marginal." The terms "high-risk recipient" and "marginal donor" have been used broadly in the literature making it virtually impossible to make comparisons between publications. In general, the data support that patients who could be easily agreed to be "sicker recipients" are at more risk compared to those who are clearly "healthier," albeit still "sick enough" to need transplantation. Given this variability in the literature, we were unable to define how being a "high-risk" recipient interplays with accepting a "marginal donor." Existing risk scores are described, but none were felt to adequately predict outcomes from factors available at the time of offer acceptance. CONCLUSIONS: We could not determine what makes a donor "marginal," a recipient "high-risk," or how these factors interplay within the specific recipient-donor pair to determine outcomes. Until there are better risk scores predicting outcomes at the time of organ acceptance, programs should continue to evaluate each organ and recipient individually.
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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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".