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Record W4381681857 · doi:10.1097/hep.0000000000000520

A multisociety Delphi consensus statement on new fatty liver disease nomenclature

2023· article· en· W4381681857 on OpenAlexaff
Mary E. Rinella, Jeffrey V. Lazarus, Vlad Ratziu, Sven Francque, Arun J. Sanyal, Fasiha Kanwal, Diana Romero, Manal F. Abdelmalek, Quentin M. Anstee, Juan Pablo Arab, Marco Arrese, Ramón Bataller, Ulrich Beuers, Jérôme Boursier, Elisabetta Bugianesi, Christopher D. Byrne, Graciela Castro‐Narro, Abhijit Chowdhury, Helena Cortez‐Pinto, Donna R. Cryer, Kenneth Cusi, Mohamed El‐Kassas, Samuel Klein, Wayne Eskridge, Jian‐Gao Fan, Samer Gawrieh, Cynthia D. Guy, Stephen A. Harrison, Seung Up Kim, Bart G.P. Koot, Marko Korenjak, Kris V. Kowdley, Florence Lacaille, Rohit Loomba, Robert Mitchell-Thain, Timothy R. Morgan, Elisabeth E. Powell, Michael Roden, Manuel Romero‐Gómez, Marcelo Silva, Shivaram Prasad Singh, Silvia Sookoian, C Wendy Spearman, Dina Tiniakos, Luca Valenti, Miriam B. Vos, Vincent Wai‐Sun Wong, Stavra A. Xanthakos, Yusuf Yılmaz, Zobair M. Younossi, Ansley Hobbs, Marcela Villota‐Rivas, Philip N. Newsome

Bibliographic record

VenueHepatology · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsLondon Health Sciences CentreWestern University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesSwedish Orphan BiovitrumNovo Nordisk FondenEuropean Association for the Study of the LiverAllerganShionogiAstellas PharmaEisaiNovo NordiskNational Institute for Health and Care ResearchPfizerAmerican Association for the Study of Liver DiseasesNGM BiopharmaceuticalsBristol-Myers SquibbEli Lilly and CompanyAstraZenecaCSL BehringGilead SciencesCelgeneGenentechCoherus Biosciences
KeywordsNomenclatureStatement (logic)MedicineInternal medicinePolitical scienceBiologyTaxonomy (biology)ZoologyLaw

Abstract

fetched live from OpenAlex

The principal limitations of the terms NAFLD and NASH are the reliance on exclusionary confounder terms and the use of potentially stigmatising language. This study set out to determine if content experts and patient advocates were in favor of a change in nomenclature and/or definition. A modified Delphi process was led by three large pan-national liver associations. The consensus was defined a priori as a supermajority (67%) vote. An independent committee of experts external to the nomenclature process made the final recommendation on the acronym and its diagnostic criteria. A total of 236 panelists from 56 countries participated in 4 online surveys and 2 hybrid meetings. Response rates across the 4 survey rounds were 87%, 83%, 83%, and 78%, respectively. Seventy-four percent of respondents felt that the current nomenclature was sufficiently flawed to consider a name change. The terms "nonalcoholic" and "fatty" were felt to be stigmatising by 61% and 66% of respondents, respectively. Steatotic liver disease was chosen as an overarching term to encompass the various aetiologies of steatosis. The term steatohepatitis was felt to be an important pathophysiological concept that should be retained. The name chosen to replace NAFLD was metabolic dysfunction-associated steatotic liver disease. There was consensus to change the definition to include the presence of at least 1 of 5 cardiometabolic risk factors. Those with no metabolic parameters and no known cause were deemed to have cryptogenic steatotic liver disease. A new category, outside pure metabolic dysfunction-associated steatotic liver disease, termed metabolic and alcohol related/associated liver disease (MetALD), was selected to describe those with metabolic dysfunction-associated steatotic liver disease, who consume greater amounts of alcohol per week (140-350 g/wk and 210-420 g/wk for females and males, respectively). The new nomenclature and diagnostic criteria are widely supported and nonstigmatising, and can improve awareness and patient identification.

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)0.321
metaresearch head score (Gemma)0.168
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.321
Threshold uncertainty score0.837

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3210.168
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.003
Science and technology studies0.0060.006
Scholarly communication0.0060.007
Open science0.0040.018
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.051
GPT teacher head0.318
Teacher spread0.267 · 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.

Study designQualitative
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

Citations2,904
Published2023
Admission routes1
Has abstractyes

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