Risk Factors for Rejection and Infection in Pediatric Liver Transplantation
Bibliographic record
Abstract
Rejection and infection are important adverse events after pediatric liver transplantation, not previously subject to concurrent risk analysis. Of 2291 children (<18 years), rejection occurred at least once in 46%, serious bacterial/fungal or viral infections in 52%. Infection caused more deaths than rejection (5.5% vs. 0.6% of patients, p < 0.001). Early rejection (<6 month) did not contribute to mortality or graft failure. Recurrent/chronic rejection was a risk in graft failure, but led to retransplant in only 1.6% of first grafts. Multivariate predictors of bacterial/fungal infection included recipient age (highest in infants), race, donor organ variants, bilirubin, anhepatic time, cyclosporin (vs. tacrolimus) and era of transplant (before 2002 vs. after 2002); serious viral infection predictors included donor organ variants, rejection, Epstein-Barr Virus (EBV) naivety and era; for rejection, predictors included age (lowest in infants), primary diagnosis, donor-recipient blood type mismatch, the use of cyclosporin (vs. tacrolimus), no induction and era. In pediatric liver transplantation, infection risk far exceeds that of rejection, which causes limited harm to the patient or graft, particularly in infants. Aggressive infection control, attention to modifiable factors such as pretransplant nutrition and donor organ options and rigorous age-specific review of the risk/benefit of choice and intensity of immunosuppressive regimes is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".