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Record W1678445379 · doi:10.1002/lt.23849

Liver graft allocation for hepatocellular carcinoma patients: Is a forced ablate-and-wait protocol nationally applicable?

2014· letter· en· W1678445379 on OpenAlexaff
Fateh Bazerbachi, Elizabeth S. Aby, John R. Lake

Bibliographic record

VenueLiver Transplantation · 2014
Typeletter
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsUniversity Health NetworkUniversity of TorontoToronto General Hospital
FundersUniversity of Minnesota
KeywordsMedicineLiver transplantationHepatocellular carcinomaCohortTransplantationMilan criteriaInternal medicineOncologySurgeryIntensive care medicine

Abstract

fetched live from OpenAlex

We welcome the comments from Mehta et al. regarding our editorial1 and this opportunity to further discuss our opinions. In the original article, Mehta et al.2 identified a specific cohort of hepatocellular carcinoma (HCC) patients with T2 tumors whose risk of wait-list dropout was very low, and they suggested that such patients should not receive the same allocation advantage as higher risk groups (ie, those not meeting their criteria). In our editorial, we raised the issue of whether using such data to inform a new allocation algorithm might lead to worsening outcomes through the selection of recipients with a higher risk of tumor recurrence for liver transplantation (LT). The authors have now provided outcome data to supplement their study and compared the identified outcomes of transplantation for their low-risk subgroup with those for the remaining HCC patients. Although the recurrence rates did not appear different, the non–low-risk recipients had worse 5-year patient survival, with HCC recurrence representing the most common cause of death. We disagree with their conclusion that “excluding patients with a very low risk of wait-list dropout from LT does not mean that we would instead perform transplantation for patients with a high risk of posttransplant HCC recurrence and poor survival,” and here we will focus on 2 primary issues. First, the primary outcome that these investigators should have reported is recurrence-free patient survival for the 2 cohorts. Because 5-year survival was significantly worse for the non–low-risk recipients and the recurrence rates were numerically higher for the non–low-risk recipients, recurrence-free survival will undoubtedly be significantly worse for the non–low-risk recipients. This actually validates our concern. Second, these additional data are relatively unique to the University of California San Francisco (UCSF) for 2 reasons. LT outcomes at UCSF are excellent, as evidenced by the 94% 5-year patient survival rate for the low-risk cohort, and as such, the statement that “rates … for all others compare favorably with published United Network for Organ Sharing data” really has no meaning because it is the equivalent of comparing apples to oranges. More importantly, centers in the donor service area in which UCSF functions perform transplantation with one of the highest (if not the highest) mean Model for End-Stage Liver Disease scores in the country. In the original study, patients at UCSF underwent LT with a median waiting time of 8.8 months (interquartile range = 5.9-12.8 months). During the waiting period, 16.6% dropped out because of tumor progression, and 6.5% died (23.1% in all). The median time from listing to dropout (due to death or tumor progression) was 7.2 months (interquartile range = 3.5-10.6 months). However, a recent liver simulated allocation modeling analysis testing the effects of mandatory waiting times on transplant rates showed that with the current allocation system (before the initiation of Share 35), the mean match Model for End-Stage Liver Disease score for patients undergoing transplantation for HCC in the United States was 24 (unpublished data). This indicates that the majority of patients undergoing transplantation for HCC in the United States do so within 3 months of receiving a Model for End-Stage Liver Disease exception score. In a previous article,3 these authors endorsed the ablate-and-wait principle, which allows the preselection of candidates with a more favorable tumor biology for transplantation. Indeed, the longer waiting periods that characterize the UCSF donor service area provide a de facto forced ablate-and-wait protocol. In such a system, which is not characteristic of the country in general, favorable outcomes are conceivable. In our opinion, the authors' data are relatively unique to their center because of the prolonged average wait-list time for LT candidates and their excellent outcomes. Even so, we believe that these data actually support our premise that blindly using these data to inform allocation policy could lead to worse posttransplant outcomes. We strongly believe that further examinations of outcomes across all regions and donor service areas with any newly proposed allocation system will be necessary for policymaking. Fateh Bazerbachi, M.D.1 Elizabeth Aby, B.A.1 John Lake, M.D.1 1Department of Medicine University of Minnesota Minneapolis, MN

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 imitation

Not 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.

metaresearch head score (Codex)0.183
metaresearch head score (Gemma)0.498
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.183
Threshold uncertainty score0.965

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1830.498
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0010.002
Science and technology studies0.0030.004
Scholarly communication0.0070.006
Open science0.0060.004
Research integrity0.0230.022
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.036
GPT teacher head0.243
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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