Decline of increased risk donor offers increases waitlist mortality in paediatric heart transplantation
Bibliographic record
Abstract
BACKGROUND: Increased risk donors in paediatric heart transplantation have characteristics that may increase the risk of infectious disease transmission despite negative serologic testing. However, the risk of disease transmission is low, and refusing an IRD offer may increase waitlist mortality. We sought to determine the risks of declining an initial IRD organ offer. METHODS AND RESULTS: We performed a retrospective analysis of candidates waitlisted for isolated PHT using 20072017 United Network of Organ Sharing datasets. Match runs identified candidates receiving IRD offers. Competing risks analysis was used to determine mortality risk for those that declined an initial IRD offer with stratified Cox regression to estimate the survival benefit associated with accepting initial IRD offers. Overall, 238/1067 (22.3%) initial IRD offers were accepted. Candidates accepting an IRD offer were younger (7.2 versus 9.8 years, p < 0.001), more often female (50 versus 41%, p = 0.021), more often listed status 1A (75.6 versus 61.9%, p < 0.001), and less likely to require mechanical bridge to PHT (16% versus 23%, p = 0.036). At 1- and 5-year follow-up, cumulative mortality was significantly lower for candidates who accepted compared to those that declined (6% versus 13% 1-year mortality and 15% versus 25% 5-year mortality, p = 0.0033). Decline of an IRD offer was associated with an adjusted hazard ratio for mortality of 1.87 (95% CI 1.24, 2.81, p < 0.003). CONCLUSIONS: IRD organ acceptance is associated with a substantial survival benefit. Increasing acceptance of IRD organs may provide a targetable opportunity to decrease waitlist mortality in PHT.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".