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Record W4415629696 · doi:10.1097/lvt.0000000000000762

Strategic leadership and financial governance in liver transplantation: Global insights from the International Liver Transplantation Society Congress 2025

2025· article· en· W4415629696 on OpenAlexaff
Aghnia J. Putri, Zoltán Czigány, Alfred Wei Chieh Kow, Daniel G. Maluf, Markus Selzner

Bibliographic record

VenueLiver Transplantation · 2025
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLiver transplantationReimbursementCorporate governanceSustainabilityHealth careMultidisciplinary approachTransplantation

Abstract

fetched live from OpenAlex

The 2025 International Liver Transplantation Society (ILTS) Congress, held in Singapore, brought together a global, multidisciplinary community to explore innovations and persistent challenges in liver transplantation (LT). The congress included seven pre-congress workshops, 92 scientific sessions, and featured 270 expert speakers. More than 1100 participants from 71 countries took part in the event. A new focus was the growing importance of strategic leadership and financial governance in sustaining and expanding liver transplant programs. A series of presentations, symposiums, and workshops focused on leadership and financial governance brought together clinical and administrative leaders to explore the operational frameworks and economic strategies critical to the long-term sustainability of liver transplant programs. Discussions emphasized the need for robust reimbursement models, clear cost-effectiveness frameworks, and integration of emerging technologies into diverse healthcare systems.

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)0.012
metaresearch head score (Gemma)0.007
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.013
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.006
Scholarly communication0.0130.008
Open science0.0010.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.267
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
GenreCommentary

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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