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Record W4408294343 · doi:10.1097/lvt.0000000000000593

Proceedings of the 29th Annual Congress of the International Liver Transplantation Society

2025· article· en· W4408294343 on OpenAlexaff
Madhukar S. Patel, Sadhana Shankar, Marta Tejedor, Andrew S. Barbas, Joohyun Kim, Shennen A. Mao, Tommy Ivanics, Johns Shaji Mathew, Alexandra Shingina, Mohammad Qasim Khan, Elizabeth A. Wilson, Nicholas Syn, Felipe Alconchel, Dhupal Patel, Jiang Liu, David Nasralla, Alessandra Mazzola, Tomohiro Tanaka, David W. Victor, Carmen Vinaixa, Aye Mya Mya Kyaw, Antonio Galante, Paolo Magistri, Manikandan Kathirvel, Daniel Aliseda, Kenan Moral, Tommaso Di Maira, Eléonora De Martin, Ryan Chadha, Abdul Hakeem, Eliano Bonaccorsi‐Riani, Ashwin Rammohan

Bibliographic record

VenueLiver Transplantation · 2025
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsWestern UniversityToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsVanguardMedicineLiver transplantationTransplantationTheme (computing)Liver diseaseGeneral surgerySurgeryInternal medicineHistory

Abstract

fetched live from OpenAlex

The 2024 Annual Congress of the International Liver Transplantation Society (ILTS) was from May 1-4 in Houston, Texas, USA, under the theme "Liver Disease and Transplantation: Breaking Barriers and Exploring New Frontiers." In addition to a robust scientific program, the congress also hosted a hands-on cadaveric robotic liver surgery course, a machine perfusion workshop, and a transesophageal echocardiography course. In this report, the ILTS Vanguard and Basic Sciences Committees present a summary of the congress proceedings.

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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.055
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0550.016

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 head0.007
GPT teacher head0.247
Teacher spread0.240 · 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
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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