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Record W2089416968 · doi:10.1002/hep.27191

Evaluation of the Pediatric Patient for Liver Transplantation: 2014 Practice Guideline by the American Association for the Study of Liver Diseases, American Society of Transplantation and the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition

2014· article· en· W2089416968 on OpenAlexaff
Robert H. Squires, Vicky L. Ng, René Romero, Udeme D. Ekong, Winita Hardikar, Sukru Emre, George Mazariegos

Bibliographic record

VenueHepatology · 2014
Typearticle
Languageen
FieldMedicine
TopicPediatric Hepatobiliary Diseases and Treatments
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick Children
FundersNorth American Society for Pediatric Gastroenterology, Hepatology and NutritionAmerican Association for the Study of Liver Diseases
KeywordsMedicineLiver transplantationGuidelineTransplantationFamily medicineIntensive care medicineAssociation (psychology)Internal medicinePathologyPsychology

Abstract

fetched live from OpenAlex

Current American Association for the Study of Liver Diseases (AASLD) liver transplant evaluation guidelines include both adult and pediatric patients. 1 While pediatric liver transplants account for 7.8% of all liver transplants in the United States, sufficient differences between pediatric and adult patients seeking liver transplantation (LT) now require independent, yet complementary documents. This document will focus on pediatric issues at each level of the evaluation process. Disease categories suitable for referral to a pediatric LT program are similar to adults: acute liver failure, autoimmune, cholestasis, metabolic or genetic, oncologic, vascular, and infectious. However, specific etiologies and outcomes differ widely from adult patients, justifying independent pediatric guidelines. Data supporting our recommendations are based on a Medline search of the English language literature from 1997 to the present.

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)0.005
metaresearch head score (Gemma)0.014
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 score0.009
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.003

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 head0.014
GPT teacher head0.291
Teacher spread0.278 · 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

Citations246
Published2014
Admission routes1
Has abstractyes

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