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Record W3122013602 · doi:10.1016/j.jhepr.2021.100234

European ‘NAFLD Preparedness Index’ — Is Europe ready to meet the challenge of fatty liver disease?

2021· article· en· W3122013602 on OpenAlex
Jeffrey V. Lazarus, Adam Palayew, Patrizia Carrieri, Mattias Ekstedt, Giulio Marchesini, Katja Novak, Vlad Ratziu, Manuel Romero‐Gómez, Frank Tacke, Shira Zelber‐Sagi, Helena Cortez‐Pinto, Quentin M. Anstee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJHEP Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsMcGill University
FundersAllerganEuropean Association for the Study of the LiverNational Institute for Health and Care ResearchGeneralitat de CatalunyaEuropean Social FundGilead SciencesCentres de Recerca de CatalunyaNIHR Newcastle Biomedical Research CentreMinisterio de Ciencia, Innovación y UniversidadesInstituto de Salud Carlos IIIEuropean CommissionPfizer
KeywordsPreparednessEpidemiologyMedicineFatty liverEnvironmental healthDiseaseIndex (typography)Metabolic syndromeObesityFamily medicinePolitical sciencePathologyComputer science

Abstract

fetched live from OpenAlex

Background & Aims Non-alcoholic fatty liver disease (NAFLD), which is closely associated with obesity, metabolic syndrome, and diabetes, is a highly prevalent emerging condition that can be optimally managed through a multidisciplinary patient-centred approach. National preparedness to address NAFLD is essential to ensure that health systems can deliver effective care. We present a NAFLD Preparedness Index for Europe. Methods In June 2019, data were extracted by expert groups from 29 countries to complete a 41-item questionnaire about NAFLD. Questions were classified into 4 categories: policies/civil society (9 questions), guidelines (16 questions), epidemiology (4 questions), and care management (12 questions). Based on the responses, national preparedness for each indicator was classified into low, middle, or high-levels. We then applied a multiple correspondence analysis to obtain a standardised preparedness score for each country ranging from 0 to 100. Results The analysis estimated a summary factor that explained 71.3% of the variation in the dataset. No countries were found to have yet attained a high-level of preparedness. Currently, the UK (75.5) scored best, although falling within the mid-level preparedness band, followed by Spain (56.2), and Denmark (43.4), whereas Luxembourg and Ireland were the lowest scoring countries with a score of 4.9. Only Spain scored highly in the epidemiology indicator category, whereas the UK was the only country that scored highly for care management. Conclusions The NAFLD Preparedness Index indicates substantial variation between countries' readiness to address NAFLD. Notably, even those countries that score relatively highly exhibit deficiencies in key domains, suggesting that structural changes are needed to optimise NAFLD management and ensure effective public health approaches are in place. Lay summary Non-alcoholic fatty liver disease (NAFLD), which is closely associated with obesity, metabolic syndrome, and diabetes, is a highly prevalent condition that can be optimally managed through a multidisciplinary patient-centred approach. National preparedness to address NAFLD is essential to allow for effective public health measures aimed at preventing disease while also ensuring that health systems can deliver effective care to affected populations. This study defined preparedness as having adequate policies and civil society engagement, guidelines, epidemiology, and care management. NAFLD preparedness was found to be deficient in all 29 countries studied, with great variation among the countries and the 4 categories studied.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.277
Teacher spread0.247 · 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