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Record W4391036030 · doi:10.1097/lvt.0000000000000330

Proceedings of the 28th Annual Congress of the International Liver Transplantation Society

2024· article· en· W4391036030 on OpenAlexaff
Johns Shaji Mathew, Alexandra Shingina, Mohammad Qasim Khan, Elizabeth A. Wilson, Nicholas Syn, Ashwin Rammohan, Felipe Alconchel, Abdul Hakeem, Sadhana Shankar, Dhupal Patel, Onur Keskin, Jiang Liu, David Nasralla, Alessandra Mazzola, Madhukar S. Patel, Tomohiro Tanaka, David W. Victor, Uzung Yoon, In‐Young Yoon, Carmen Vinaixa, Varvara A. Kirchner, Eléonora De Martin, R. Mark Ghobrial, Ryan Chadha

Bibliographic record

VenueLiver Transplantation · 2024
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsWestern University
FundersNational Institute on Aging
KeywordsMedicineLiver transplantationImmunosuppressionHepatologyLiver diseaseTransplantationIntensive care medicineMilestoneInternal medicine

Abstract

fetched live from OpenAlex

The 2023 Joint International Congress of the International Liver Transplantation Society (ILTS), the European Liver and Intestine Transplant Association (ELITA), and the Liver Intensive Care Group of Europe (LICAGE) held in Rotterdam, the Netherlands, marked a significant recovery milestone for the liver transplant community after COVID-19. With 1159 participants and a surge in abstract submissions, the event focused on "Liver Disorders and Transplantation: Innovations and Evolving Indications." This conference report provides a comprehensive overview of the key themes discussed during the event, encompassing Hepatology, Anesthesia and Critical Care, Acute Liver Failure, Infectious Disease, Immunosuppression, Pediatric Liver Transplantation, Living Donor Liver Transplantation, Transplant Oncology, Surgical Approaches, and Machine Perfusion. The congress provided a platform for extensive discussions on a wide range of topics, reflecting the continuous advancements and collaborative efforts within the liver transplant community.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.009
GPT teacher head0.249
Teacher spread0.239 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations1
Published2024
Admission routes1
Has abstractyes

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