Difference in Survival in Early Kidney after Liver Transplantation Compared with Simultaneous Liver-Kidney Transplantation: Evaluating the Potential of the “Safety Net”
Bibliographic record
Abstract
BACKGROUND: Decisions on who requires simultaneous liver-kidney (SLK) transplantation are controversial. United Network for Organ Sharing implemented a "safety net" in 2017 providing prioritization on the kidney waitlist for patients with renal failure after liver transplantation. We aimed to compare survival after early kidney after liver transplantation (KALT) and SLK. STUDY DESIGN: We compared SLK, KALT, and liver transplantation alone (LTA) in adult patients who underwent deceased donor (DD) liver transplantation in the US, from 2002 to 2018. Early KALT was defined as 60 to 365 days between liver and subsequent kidney transplantation (reflecting safety net listing criteria). Patients who died within 60 days were excluded to mitigate immortal time bias favoring KALT. RESULTS: There were 6,774 SLK, 120 KALT at 60 to 365 days, and 11,501 LTA. Early KALT had equivalent survival compared with SLK, both for all KALT (hazard ratio [HR] 0.58, 95% CI 0.34-1.00, p = 0.05) and for DD KALT only (HR 0.72, 95% CI 0.37-1.38, p = 0.32). Simultaneous liver-kidney transplantation was associated with improved survival compared with LTA (HR 0.82. 95% CI 0.76-0.87, p < 0.01). Early KALT was associated with a greater reduction in mortality compared with LTA, but this was not significant (HR 0.58, 95% CI 0.39-1.00, p = 0.05). There was a lower proportion of early KALT in African Americans relative to SLK transplantations (7% vs 16%, p = 0.04). CONCLUSIONS: Early KALT has equivalent survival compared with SLK transplantation, both for all KALT and for DD KALT only, supporting the promise of the "safety net." There was a lower proportion of African-American patients undergoing early KALT, indicating the importance of monitoring access to early KALT under the "safety net" policy.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".