Hospital readmission following pediatric heart transplantation
Bibliographic record
Abstract
The frequency, indications, and outcomes for readmission following pediatric heart transplantation are poorly characterized. A better understanding of this phenomenon will help guide strategies to address the causes of readmission. Data from the Clinical Trials in Organ Transplantation for Children (CTOTC-04) multi-institutional collaborative study were utilized to determine incidence of, and risk factors for, hospital readmission within 30 days and 1 year from initial hospital discharge. Among 240 transplants at 8 centers, 227 subjects were discharged and had follow-up. 129 subjects (56.8%) were readmitted within one year; 71 had two or more readmissions. The 30-day and 1-year freedom from readmission were 70.5% (CI: 64.1%, 76.0%) and 42.2% (CI: 35.7%, 48.7%), respectively. The most common indications for readmissions were infection followed by rejection and fever without confirmed infection, accounting for 25.0%, 10.6%, and 6.2% of readmissions, respectively. Factors independently associated with increased risk of first readmission within 1 year (Cox proportional hazard model) were as follows: transplant in infancy (P = .05), longer transplant hospitalization (P = .04), lower UNOS urgency status (2/IB vs 1A) at transplant (P = .04), and Hispanic ethnicity (P = .05). Hospital readmission occurs frequently in the first year following discharge after heart transplantation with highest risk in the first 30 days. Infection is more common than rejection as cause for readmission, with death during readmission being rare. A number of patient factors are associated with higher risk of readmission. A fuller understanding of these risk factors may help tailor strategies to reduce unnecessary hospital readmission.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".