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Record W4210497821 · doi:10.3390/diagnostics12020330

Biliary Atresia: A Complex Hepatobiliary Disease with Variable Gene Involvement, Diagnostic Procedures, and Prognosis

2022· review· en· W4210497821 on OpenAlexafffund
Consolato Sergi, Susan Gilmour

Bibliographic record

VenueDiagnostics · 2022
Typereview
Languageen
FieldMedicine
TopicPediatric Hepatobiliary Diseases and Treatments
Canadian institutionsStollery Children's HospitalChildren's Hospital of Eastern OntarioUniversity of Alberta
FundersCentre Hospitalier pour Enfants de l'est de l'Ontario
KeywordsBiliary atresiaEtiologyDiseaseMedicineAtresiaLiver diseaseBiliary diseasePediatricsGastroenterologyIntensive care medicineInternal medicineLiver transplantation

Abstract

fetched live from OpenAlex

The diagnosis of biliary atresia is still terrifying at the 3rd decade of the 21st century. In a department of neonatal intensive care unit, parents and physicians face a challenge with a jaundiced baby, who may or may not have a surgically correctable hepatopathy. The approach has been systematically evaluated, but the etiology remains ambiguous. The study of families with recurrent biliary atresia has been undertaken at a molecular level. The primary interest with this disease is to identify the etiology and change the treatment from symptomatic to curative. The occurrence of this obstructive cholangio-hepatopathy in well-known genetic syndromes has suggested just coincidental finding, but the reality can be more intriguing because some of these diseases may have some interaction with the development of the intrahepatic biliary system. Several genes have been investigated thoroughly, including ADD3 and GPC1 shifting the interest from viruses to genetics. In this review, the intriguing complexities of this hepatobiliary disease are highlighted.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.049
GPT teacher head0.299
Teacher spread0.250 · 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
GenreReview

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

Citations9
Published2022
Admission routes2
Has abstractyes

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