Joint Liver-Kidney Transplantation for Primary Hyperoxaluria and Other Metabolic Conditions
Bibliographic record
Abstract
Combined liver-kidney transplantation (CLKT) represents a pivotal therapeutic advancement in the management of metabolic disorders, particularly primary hyperoxaluria type I (PH-I), a rare autosomal recessive condition resulting in systemic oxalosis and end-stage renal disease (ESRD). This review synthesizes clinical evidence from key studies, including the University of Kansas Medical Center and international registries, to assess the efficacy, outcomes, and current management strategies associated with CLKT in PH-I and other metabolic diseases. CLKT effectively corrects the underlying hepatic enzymatic deficiency and restores renal function, significantly improving patient and graft survival rates compared to kidney-alone transplantation, which is often compromised by recurrent oxalate deposition. The review outlines perioperative management strategies such as intensive dialysis, high-volume diuresis, and pyridoxine therapy, and highlights critical considerations like timing of transplantation, oxalate mobilization, and immunosuppressive regimens. Despite ongoing challenges, including persistent post-transplant hyperoxaluria and risk of graft rejection, CLKT remains the gold standard for pyridoxine-unresponsive PH-I. Emerging approaches such as gene therapy, improved diagnostic tools, and personalized treatment protocols offer promising avenues for future care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".