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Record W4229449379 · doi:10.1111/liv.15288

Liver transplantation in patients with liver failure: Twenty years of experience from China

2022· review· en· W4229449379 on OpenAlexfundno aff
Sunbin Ling, Guangjiang Jiang, Qingyang Que, Shengjun Xu, Junli Chen, Xiao Xu

Bibliographic record

VenueLiver International · 2022
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaMcMaster University
KeywordsLiver transplantationChinaMedicineLiver diseaseLiver failureHepatitis B virusEpidemiologyDiseaseEtiologyHepatitis BTransplantationInternal medicineIntensive care medicineVirusImmunologyPolitical science

Abstract

fetched live from OpenAlex

Liver transplantation (LT) is the only effective method of treating end-stage liver disease, such as various types of liver failure. China has the largest number of patients with hepatitis B virus-related disease, which is also the main cause of liver failure. From the first LT performed in 1977, and especially over the past two decades, LT has experienced rapid development as a result of continuous research and innovation in China. China performs the second-highest number of LTs every year worldwide, and the quality of LT continues to improve. Starting January 1, 2015, all donor's livers have been from deceased donors and familial donors. Thus, China entered into a new era of LT. However, LT is still a challenging procedure in China. In this review, we introduced the brief history of LT in China, the epidemiology, aetiology and clinical outcomes of LT for liver failure in China and summarized the experience of LT from Chinese LT surgeons and scholars. The future perspectives of LT were also discussed, and it is expected that China's LT research could be further integrated elsewhere in the world.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.857
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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 designObservational
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

Citations65
Published2022
Admission routes1
Has abstractyes

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