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Record W4311373148 · doi:10.3390/curroncol29120771

Immunotherapy Use Prior to Liver Transplant in Patients with Hepatocellular Carcinoma

2022· review· en· W4311373148 on OpenAlexvenueno aff
Stephanie Woo, Alexandra V. Kimchy, Lynette M. Sequeira, C. Scott Dorris, Aiwu Ruth He, Amol S. Rangnekar

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHepatocellular carcinomaImmunotherapyMEDLINELiver transplantationClinical trialInternal medicineIntensive care medicineOncologyCancerTransplantation

Abstract

fetched live from OpenAlex

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related mortality worldwide, and its incidence has increased rapidly in the United States over the past two decades. Liver transplant is considered curative, but is not always possible, and pre-transplant immunotherapy is of great interest as a modality for downstaging the tumor burden. We present a review of the literature on pre-liver transplant immunotherapy use in patients with HCC. Our literature search queried publications in Ovid MEDLINE, Ovid Embase, and Web of Science, and ultimately identified 24 original research publications to be included for analysis. We found that the role of PD-1 and PD-L1 in risk stratification for rejection is of special interest to researchers, and ongoing randomized clinical trials PLENTY and Dulect 2020-1 will provide insight into the role of PD-1 and PD-L1 in liver transplant management in the future. This literature search and the resulting review represents the most thorough collection, analysis, and presentation of the literature on the subject to date.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.236
GPT teacher head0.355
Teacher spread0.119 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations26
Published2022
Admission routes1
Has abstractyes

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