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Record W3142703303 · doi:10.1097/tp.0000000000002549

Report of the 24th Annual Congress of the International Liver Transplantation Society

2018· article· en· W3142703303 on OpenAlexaff
Eléonora De Martin, Amelia J. Hessheimer, Ryan Chadha, Gökhan Kabaçam, Jeremy Rajanayagam, Varvara A. Kirchner, Marit Kalisvaart, Irene Scalera, Mamatha Bhat, Alan G. Contreras, Prashant Bhangui

Bibliographic record

VenueTransplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineLiver transplantationDonationTransplantationLiver diseaseOrgan donationOrgan procurementIntensive care medicineFamily medicineGeneral surgeryInternal medicinePolitical science

Abstract

fetched live from OpenAlex

The 24th Joint Annual Congress of the International Liver Transplantation Society in association with European Liver and Intestine Transplant Association and Liver Intensive Care Group of Europe was held in Lisbon, Portugal from May 23 to 26, 2018. More than 1200 participants from over 60 countries including surgeons, hepatologists, anesthesiologists and critical care intensivists, radiologists, pathologists, organ procurement personnel, and research scientists came together with the common aim of improving care and outcomes for liver transplant recipients. Over 600 scientific abstracts were presented. The principal themes were living donation, use of marginal liver donors, machine preservation, disease-specific immunosuppressive regimen, malignancies, and advances in pediatric liver transplantation and liver transplant anesthesia. This report presents excerpts from invited lectures and select abstracts from scientific sessions, which add to current knowledge, and will drive clinical practice and future research.

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)0.007
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.030
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0300.009

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 head0.012
GPT teacher head0.277
Teacher spread0.264 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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