Improving 1-Year Mortality Prediction After Pediatric Heart Transplantation Using Hypothetical Donor-Recipient Matches
Bibliographic record
Abstract
Heart transplantation is a life-saving procedure for children affected by end-stage heart failure. However, despite recent improvements in long-term outcomes, 1-year post-transplantation mortality has remained relatively high. Accurate prediction of post-transplantation mortality is crucial to evaluating risks related to recipient-donor matches. Machine learning techniques can potentially improve the current allocation system through the integration of a larger set of features. In this work, we improve 1-year mortality prediction after pediatric heart transplantation using a new self-training approach, based on generating artificial recipient-donor pairs as synthetic unlabeled observations. We tested and compared our approach to several baselines using the cohort of pediatric patients in the UNOS database. Our study suggests that augmenting the dataset with proper synthetic observations can improve the prediction of 1-year mortality after pediatric heart transplantation. Our findings have implications for the future of heart transplantation in children, offering a potential path to refine recipient-donor matching and improve survival rates. This study contributes to the growing field of advanced machine learning techniques applied to medical decision-making, specifically in the context of organ transplantation.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| 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".