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Record W3014037854 · doi:10.1097/tp.0000000000003118

Liver Transplantation for Colorectal and Neuroendocrine Liver Metastases and Hepatoblastoma. Working Group Report From the ILTS Transplant Oncology Consensus Conference

2020· review· en· W3014037854 on OpenAlexaff
Taizo Hibi, Mohamed Rela, James D. Eason, Pål‐Dag Line, John J. Fung, Seisuke Sakamoto, Nazia Selzner, Kwan Man, R. Mark Ghobrial, Gonzalo Sapisochín

Bibliographic record

VenueTransplantation · 2020
Typereview
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHepatoblastomaMedicineLiver transplantationNeuroendocrine tumorsOncologyColorectal cancerInternal medicineConsensus conferenceTransplantationChemotherapyStage (stratigraphy)DiseaseGeneral surgeryCancer

Abstract

fetched live from OpenAlex

Liver transplantation (LT) for unresectable colorectal liver metastases has long been abandoned because of dismal prognoses. After the dark ages, advances in chemotherapy and diagnostic imaging have enabled strict patient selection, and the pioneering study from the Oslo group has contributed to the substantial progress in this field. For unresectable neuroendocrine liver metastases, LT for patients who met the Milan criteria was able to achieve excellent long-term outcomes. The guidelines further adopted in the United States and Europe were based on these criteria. For hepatoblastoma, patients with unresectable and borderline-resectable disease are considered good candidates for LT; however, the indications are yet to be defined. In the budding era of transplant oncology, it is critically important to recognize the current status and unsolved questions for each disease entity. These guidelines were developed to serve as a beacon of light for optimal patient selection for LT and set the stage for future basic and clinical studies.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.076
GPT teacher head0.357
Teacher spread0.281 · 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
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

Citations49
Published2020
Admission routes1
Has abstractyes

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