Reply: Living donor liver transplantation for people with PSC
Bibliographic record
Abstract
We appreciate the interest in and discussion by Heinemann and colleagues of our recent AASLD Practice Guidance on primary sclerosing cholangitis (PSC) and cholangiocarcinoma.1 Indeed, we believe that living donor liver transplantation (LDLT) remains a promising option for PSC patients who might not otherwise have access to deceased donor liver transplantation (LT). Heinemann et al.2 queried the European Liver Transplant Registry (ELTR) and found that survival in adults and children with PSC was significantly lower after LDLT compared with deceased donor LT. They also found that LDLT recipients were at increased risk of biliary complications and recurrent PSC. These findings are intriguing and important; however, the analysis was not performed on an intention-to-treat basis and, therefore, does not account for the waitlist dropout of PSC patients awaiting deceased donor LT. In addition, the LT programs represented in the ELTR are low LDLT volume programs and LDLT volume has been associated with outcomes, including recipients undergoing LDLT for autoimmune hepatitis or cholestatic liver disease at high LDLT volume centers who had a significantly lower risk of graft failure than those transplanted at low LDLT volume centers.3 Furthermore, data from the Adult to Adult Living Donor Liver Transplantation (A2ALL) Cohort demonstrated a similar risk of recurrent PSC for recipients of either LDLT or deceased donor LT, with excellent overall survival despite recurrence.4 Finally, one of the authors (Gonzalo Sapisochin) has observed that at their high LDLT volume transplant center outcomes for PSC patients who have a potential living donor are better compared with those without a living donor option on an intention-to-treat basis (Gonzalo Sapisochin). Unlike other liver disease etiologies such as hepatitis C–associated or alcohol-associated liver disease, PSC patients tend to maintain hepatocellular synthetics function until very late stages of the disease. As a result, patients with PSC tend to have comparatively low Model for End-stage Liver Disease scores, dependent primarily on bilirubin elevation associated with recurrent cholangitis and jaundice. In this context, biological Model for End-stage Liver Disease of PSC patients may not accurately reflect the candidate’s risk of death on the waitlist. Although stratified by disease type, a recent study from Jackson et al.5 demonstrated that LDLT offers a substantial survival benefit to patients with end-stage liver disease even at Model for End-stage Liver Disease–Sodium scores as low as 11 compared with those who remained on the waitlist. This provides even more evidence to encourage LDLT for patients with PSC, especially in low Model for End-stage Liver Disease patients. There is no treatment to alter PSC’s natural history; so, disease progression is anticipated. Moreover, while the timing of overall disease progression varies, PSC patients may experience many severe symptoms, such as fatigue and pruritus, that significantly impact health-related quality of life. All studies of LT in PSC are limited by small sample sizes, wide variability in patient characteristics, and differing definitions of recurrent PSC. Differences in outcomes after LT across studies may also be skewed by referral and selection biases, variable access to LDLT, and clinical experience. We agree that more studies are needed to determine rates of recurrent PSC in patients receiving LDLT and the factors impacting recurrence rates and outcomes.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.026 | 0.034 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".