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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it