Impact of early immunosuppression on pediatric liver transplant outcomes within 1 year
Bibliographic record
Abstract
OBJECTIVES: The Starzl Network for Excellence in Pediatric Transplantation identified optimizing immunosuppression (IS) as a priority practice improvement area for patients, families, and providers. We aimed to evaluate associations between clinical characteristics, early IS, and outcomes. METHODS: We analyzed pediatric liver transplant (LT) data from 2013 to 2018 in the United Network for Organ Sharing (UNOS) and the Society of Pediatric Liver Transplantation (SPLIT) registries. RESULTS: We included 2542 LT recipients in UNOS and 1590 in SPLIT. IS choice varied between centers with steroid induction and mycophenolate mofetil (MMF) use each ranging from 0% to 100% across centers. Clinical characteristics associated with early IS choice were inconsistent between the two data sets. T-cell depleting antibody use was associated with improved 1-year graft (hazard ratio [HR] 0.50, 95% confidence interval [CI] 0.34-0.76) and patient (HR 0.40, 95% CI 0.20-0.79) survival in UNOS but decreased 1-year patient survival (HR 4.12, 95% CI 1.31-12.93) and increased acute rejection (HR 1.58, 95% CI 1.07-2.34) in SPLIT. Non-T-cell depleting antibody use was not associated with differential risk of survival nor rejection. MMF use was associated with improved 1-year graft survival (HR 0.73, 95% CI 0.54-0.99) in UNOS only. CONCLUSIONS: Variation exists in center choice of early IS regimen. UNOS and SPLIT data provide conflicting associations between IS and outcomes in multivariable analysis. These results highlight the need for future multicenter collaborative work to identify evidence-based IS best practices.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".