Review of the impact of donor characteristics on pediatric heart transplant outcomes
Bibliographic record
Abstract
Heart transplantation (HTx) is a treatment option for end-stage heart failure in children. HTx is limited by the availability and acceptability of donor hearts. Refusal of donor hearts has been reported to be common with reasons for refusal including preexisting donor characteristics. This review will focus on the impact of donor characteristics and comorbidities on outcomes following pediatric HTx. A literature review was performed to identify articles on donor characteristics and comorbidities and pediatric HTx outcomes. There are many donor characteristics to consider when accepting a donor heart. Weight-based matching is the most common form of matching in pediatric HTx with a donor-recipient weight ratio between 0.7 and 3 having limited impact on outcomes. From an age perspective, donors <50 years can be carefully considered, but the impact of ischemic time needs to be understood. To increase the donor pool, with minimal impact on outcomes, ABO-incompatible donors should be considered in patients that are eligible. Other factors to be considered when accepting an organ is donor comorbidities. Little is known about donor comorbidities in pediatric HTx, with most of the data available focusing on infections. Being aware of the potential infections in the donor, understanding the testing available and risks of transmission, and treatment options for the recipient is essential. There are a number of donor characteristics that potentially impact outcomes following pediatric HTx, but these need to be taken into consideration along with their interactions with recipient factors when interpreting the outcomes following HTx.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".