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Record W2558403761 · doi:10.1097/mpg.0000000000001482

NASPGHAN Clinical Practice Guideline for the Diagnosis and Treatment of Nonalcoholic Fatty Liver Disease in Children

2016· article· en· W2558403761 on OpenAlexaff
Miriam B. Vos, Stephanie H. Abrams, Sarah E. Barlow, Sonia Caprio, Stephen R. Daniels, Rohit Kohli, Marialena Mouzaki, Pushpa Sathya, Jeffrey B. Schwimmer, Shikha S. Sundaram, Stavra A. Xanthakos

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2016
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsJaneway Children's Health and Rehabilitation CentreMemorial University of NewfoundlandSickKids FoundationHospital for Sick Children
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMedicineNonalcoholic fatty liver diseaseGuidelineDiseaseInsulin resistanceChronic liver diseaseFatty liverIntensive care medicinePopulationLiver diseaseClinical PracticeInternal medicinePediatricsFamily medicinePathologyObesityEnvironmental healthCirrhosis

Abstract

fetched live from OpenAlex

Nonalcoholic fatty liver disease (NAFLD) is a highly prevalent chronic liver disease that occurs in the setting of insulin resistance and increased adiposity. It has rapidly evolved into the most common liver disease seen in the pediatric population and is a management challenge for general pediatric practitioners, subspecialists, and for health systems. In this guideline, the expert committee on NAFLD reviewed and summarized the available literature, formulating recommendations to guide screening and clinical care of children with NAFLD.

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)0.003
metaresearch head score (Gemma)0.015
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: Methods · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0060.005

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 head0.031
GPT teacher head0.337
Teacher spread0.306 · 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
GenreMethods

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

Citations1,001
Published2016
Admission routes1
Has abstractyes

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