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Record W3160986817 · doi:10.1002/hep4.1719

Association Between Transient Elastography and Controlled Attenuated Parameter and Liver Ultrasound in Children With Cystic Fibrosis

2021· article· en· W3160986817 on OpenAlexaff
Wen Ye, Daniel H. Leung, Jean P. Molleston, Simon C. Ling, Karen F. Murray, Jennifer L. Nicholas, Suiyuan Huang, Boaz Karmazyn, Roger K. Harned, Prakash Masand, Adina Alazraki, Oscar M. Navarro, Randolph K. Otto, Joseph J. Palermo, Alexander J. Towbin, Estella M. Alonso, Wikrom Karnsakul, Sarah Jane Schwarzenberg, Glenn F. Seidel, Marilyn J. Siegel, John C. Magee, Michael R. Narkewicz, A. Jay Freeman

Bibliographic record

VenueHepatology Communications · 2021
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesGilead SciencesNational Institutes of HealthCystic Fibrosis Foundation
KeywordsTransient elastographyCystic fibrosisLiver fibrosisTransient (computer programming)UltrasoundMedicineElastographyPathologyRadiologyFibrosisInternal medicineComputer science

Abstract

fetched live from OpenAlex

Methods to identify children with cystic fibrosis (CF) at risk for development of advanced liver disease are lacking. We aim to determine the association between liver stiffness measurement (LSM) by vibration‐controlled transient elastography (VCTE) with research ultrasound (US) patterns and conventional hepatic markers as a potential means to follow liver disease progression in children with CF. ELASTIC (Longitudinal Assessment of Transient Elastography in CF) is a nested cohort of 141 patients, ages 7‐21, enrolled in the Prediction by US of Risk of Hepatic Cirrhosis in CF (PUSH) Study. We studied the association between LSM with research‐grade US patterns (normal [NL], heterogeneous [HTG], homogeneous [HMG], or nodular [NOD]) and conventional hepatic markers. In a subgroup (n = 79), the association between controlled attenuation parameter (CAP) and US pattern was explored. Among 133 subjects undergoing VCTE, NOD participants (n = 26) had a significantly higher median (interquartile range) LSM of 9.1 kPa (6.3, 15.8) versus NL (n = 72, 5.1 kPa [4.2, 7.0]; P < 0.0001), HMG (n = 17, 5.9 kPa [5.2, 7.8]; P = 0.0013), and HTG (n = 18, 6.1 kPa [4.7, 7.0]; P = 0.0008) participants. HMG participants (n = 14) had a significantly higher mean CAP (SD) (270.5 dB/m [61.1]) compared with NL (n = 40, 218.8 dB/m [46.5]; P = 0.0027), HTG (n = 10, 218.1 dB/m [60.7]; P = 0.044), and NOD (n = 15, 222.7 dB/m [56.4]; P = 0.041) participants. LSM had a negative correlation with platelet count (rs = 0.28, P = 0.0071) and positive correlation with aspartate aminotransferase–to‐platelet ratio index (rs = 0.38, P = 0.0002), Fibrosis‐4 index (rs = 0.36, P = 0.0007), gamma‐glutamyltransferase (GGT; rs = 0.35, P = 0.0017), GGT‐to‐platelet ratio (rs = 0.35, P = 0.003), and US spleen size z‐score (rs = 0.27, P = 0.0073). Conclusion: VCTE is associated with US patterns and conventional markers in patients with liver disease with CF.

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)0.001
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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
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

Citations12
Published2021
Admission routes1
Has abstractyes

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