Waitlist Outcomes for Pediatric Heart Transplantation in the Current Era: An Analysis of the Pediatric Heart Transplant Society Database
Bibliographic record
Abstract
BACKGROUND: Waitlist mortality (WM) remains elevated in pediatric heart transplantation. Allocation policy is a potential tool to help improve WM. This study aims to identify patients at highest risk for WM to potentially inform future allocation policy changes. METHODS: was defined as death while awaiting transplant or removal from the waitlist due to clinical deterioration. Because WM is low after the first year, analysis was limited to the first 12 months on the heart transplant list. Kaplan-Meier analysis and log-rank testing was conducted to compare unadjusted survival between groups. Cox proportional hazard models were created to determine risk factors for WM. Subgroup analysis was performed for status 1A patients based on body surface area (BSA) at time of listing, cardiac diagnosis, and presence of mechanical circulatory support. RESULTS: BSA groups. VAD support was associated with lower WM other than in the single ventricle cohort, where VAD was associated with higher WM. Extracorporeal membrane oxygenation and mechanical ventilation were associated with increased risk of WM in all cohorts. CONCLUSIONS: There is significant variability in WM among status-1A patients. Potential refinements to current allocation system should factor in the increased WM risk we identified in patients supported by extracorporeal membrane oxygenation or mechanical ventilation, single ventricle congenital heart disease on VAD support and small children with congenital heart disease, restrictive cardiomyopathy, or hypertrophic cardiomyopathy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".