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Record W4399560231 · doi:10.1136/bmjopen-2023-081933

Incidence, management and outcomes in hepatic artery complications after paediatric liver transplantation: protocol of the retrospective, international, multicentre HEPATIC Registry

2024· article· en· W4399560231 on OpenAlexaff
Weihao Li, Hubert P. J. van der Doef, Barbara E. Wildhaber, Paolo Marra, M. Bravi, D. Pinelli, Julia Minetto, Marcelo Dip, Sergio Sierre, Martín de Santibañes, Victoria Ardiles, Jimmy Walker Uño, Winita Hardikar, Sue Bates, Lynette Goh, Denise Aldrian, Jonathan Seisenbacher, Georg F. Vogel, João Seda Neto, Eduardo A. Fonseca, Carolina Magalhães Costa, Cristina Targa Ferreira, Luiza Salgado Nader, Marco Farina, Khaled Dajani, Alessandro Parente, David L. Bigam, Ting-Bo Liang, Xueli Bai, Wei Zhang, Lucie Gonsorčíková, Jiří Froněk, Šimon Bohuš, Stéphanie Franchi‐Abella, Emmanuel Gonzalès, Florent Guérin, Norman Junge, Ulrich Baumann, Nicolas Richter, Steffen Hartleif, Ekkehard Sturm, Muthukumarassamy Rajakannu, Kumar Palaniappan, Mohamed Rela, Arti Pawaria, Haritha Rajakrishnan, S Sudhindran, Mukesh Kumar, Shaleen Agarwal, Subhash Gupta, Sonal Asthana, Vaishnavi Bandewar, Karthik Raichurkar, Marco Spada, Lidia Monti, Tommaso Alterio, Yusuke Yanagi, Hajime Uchida, Ryuji Komine, Helen Evans, Peter Carr‐Boyd, David Duncan, Marek Stefanowicz, Julita Latka-Grot, Adam Koleśnik, Dieter C. Bröering, Dimitri Aristotle Raptis, Kris Ann Hervera Marquez, Vidyadhar Padmakar Mali, Marion Aw, Marisa Beretta, Francisca van der Schyff, Jesús Quintero-Bernabeu, María Mercadal‐Hally, Mauricio Larrarte King, Ane Miren Andrés, F Hernández, E. Frauca, Thomas Casswall, Carl Jorns, Martin Delle, Girish Gupte, Khalid Sharif, Simon P. McGuirk, Riccardo Superina, Juan Carlos Caicedo, Catalina Jaramillo, Leandra Bitterfeld, Zachary J. Kastenberg, Amit A. Shah, Bryanna Domenick, Michael R. Acord, George Mazariegos, Kyle Soltys, Joseph DiNorcia, Swanti Antala, Sander Florman, Bettina M. Buchholz, Uta Herden, Lutz Fischer, Rudi Dierckx, Hermien Hartog, Reinoud P.H. Bokkers

Bibliographic record

VenueBMJ Open · 2024
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineLiver transplantationIncidence (geometry)Observational studyTransplantationRetrospective cohort studyPediatricsSurgeryEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Hepatic artery complications (HACs), such as a thrombosis or stenosis, are serious causes of morbidity and mortality after paediatric liver transplantation (LT). This study will investigate the incidence, current management practices and outcomes in paediatric patients with HAC after LT, including early and late complications. METHODS AND ANALYSIS: The HEPatic Artery stenosis and Thrombosis after liver transplantation In Children (HEPATIC) Registry is an international, retrospective, multicentre, observational study. Any paediatric patient diagnosed with HAC and treated for HAC (at age <18 years) after paediatric LT within a 20-year time period will be included. The primary outcomes are graft and patient survivals. The secondary outcomes are technical success of the intervention, primary and secondary patency after HAC intervention, intraprocedural and postprocedural complications, description of current management practices, and incidence of HAC. ETHICS AND DISSEMINATION: All participating sites will obtain local ethical approval and (waiver of) informed consent following the regulations on the conduct of observational clinical studies. The results will be disseminated through scientific presentations at conferences and through publication in peer-reviewed journals. TRIAL REGISTRATION NUMBER: The HEPATIC registry is registered at the ClinicalTrials.gov website; Registry Identifier: NCT05818644.

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)0.032
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.032
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.006
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.023
GPT teacher head0.362
Teacher spread0.339 · 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 designObservational
Domainnot available
GenreProtocol

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
Published2024
Admission routes1
Has abstractyes

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