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Record W2995224034 · doi:10.1097/tp.0000000000003058

What Is Hot and New in Basic and Translational Science in Liver Transplantation in 2019? Report of the Basic and Translational Research Committee of the International Liver Transplantation Society

2019· article· en· W2995224034 on OpenAlexaffabout
Timuçin Taner, Paulo N. Martins, Qi Ling, TP Ng, Kuang‐Tzu Huang, Corey Eymard, Mamatha Bhat, Eliano Bonaccorsi‐Riani, Valeria R. Mas, Markus Selzner, Burcin Ekser

Bibliographic record

VenueTransplantation · 2019
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsTranslational researchLiver transplantationTransplantationBasic researchTranslational scienceMedicineInternal medicinePathologyLibrary scienceComputer science

Abstract

fetched live from OpenAlex

The International Liver Transplantation Society (ILTS) 2019 Annual Congress was held in Toronto, Canada, in May 2019. Members of the ILTS Basic and Translational Research Committee attended all sessions of the meeting and selected the most promising, innovative, and novel research presented. A total of 900 abstracts were presented at the meeting. The percentage of abstracts presented at the ILTS Congress that contains basic or translational research continues to increase, accounting for 15% of all the abstracts in 2019, up from 10% in 2018. Here, we summarize the "what's hot what's new" in 5 main themes: liver immunobiology and tolerance, ischemia/reperfusion injury and organ preservation, bioengineering and liver regeneration, hepatic primary tumor biology, and pathophysiology of liver failure.

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)0.077
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score0.406

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.006
Science and technology studies0.0040.005
Scholarly communication0.0150.012
Open science0.0020.006
Research integrity0.0090.013
Insufficient payload (model declined to judge)0.0080.003

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 head0.037
GPT teacher head0.324
Teacher spread0.287 · 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.

Study designNot applicable
DomainMethods
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

Citations7
Published2019
Admission routes2
Has abstractyes

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