Risk factors for specific causes of death following pediatric heart transplant: An analysis of the registry of the <scp>I</scp>nternational <scp>S</scp>ociety of <scp>H</scp>eart and <scp>L</scp>ung <scp>T</scp>ransplantation
Bibliographic record
Abstract
We sought to determine temporal changes in COD and identify COD-specific risk factors in pediatric primary HTx recipients. Using the ISHLT registry, time-dependent hazard of death after pediatric HTx, stratified by COD, was analyzed by multiphasic parametric hazard modeling with multivariable regression models for risk factor analysis. The proportion of pediatric HTx deaths from each of cardiovascular cause, allograft vasculopathy, and malignancy increased over time, while all other COD decreased post-HTx. Pre-HTx ECMO was associated with increased risk of death from graft failure (HR 2.43; p < 0.001), infection (HR 2.85; p < 0.001), and MOF (HR 2.22; p = 0.001), while post-HTx ECMO was associated with death from cerebrovascular events/bleed (HR 2.55; p = 0.001). CHD was associated with deaths due to pulmonary causes (HR 1.78; p = 0.007) or infection (HR 1.72; p < 0.001). Non-adherence was a significant risk factor for all cardiac COD, notably graft failure (HR 1.66; p = 0.001) and rejection (HR 1.89; p < 0.001). Risk factors related to specific COD are varied across different temporal phases post-HTx. Increased understanding of these factors will assist in risk stratification, guide anticipatory clinical decisions, and potentially improve patient survival.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".