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Record W2332162338 · doi:10.1093/pch/21.3.131

The Canadian Biliary Atresia Registry: Improving the care of Canadian infants with biliary atresia

2016· article· en· W2332162338 on OpenAlexaffabout
Alison Butler, Richard A. Schreiber, Natalie Yanchar, Sherif Emil, Jean-Martin Laberge

Bibliographic record

VenuePaediatrics & Child Health · 2016
Typearticle
Languageen
FieldMedicine
TopicPediatric Hepatobiliary Diseases and Treatments
Canadian institutionsMontreal Children's HospitalDalhousie UniversityUniversity of British Columbia
Fundersnot available
KeywordsBiliary atresiaMedicineLiver transplantationReferralBiliary cirrhosisLiver diseasePediatricsPopulationIntensive care medicineFamily medicineDiseaseInternal medicineTransplantationEnvironmental health

Abstract

fetched live from OpenAlex

Biliary atresia is the most common cause of end-stage liver disease and liver cirrhosis in children, and the leading indication for liver transplantation in the paediatric population. There is no cure for biliary atresia; however, timely diagnosis and early infant age at surgical intervention using the Kasai portoenterostomy optimize the prognosis. Late referral is a significant problem in Canada and elsewhere. There is also a lack of standardized care practices among treating centres in this country. Biliary atresia registries currently exist across Europe, Asia and the United States. They have provided important evidence-based information to initiate changes to biliary atresia care in their countries with improvements in outcome. The Canadian Biliary Atresia Registry was initiated in 2013 for the purpose of identifying best standards of care, enhancing public education, facilitating knowledge translation and advocating for novel national public health policy programs to improve the outcomes of Canadian infants with biliary atresia.

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)0.004
metaresearch head score (Gemma)0.017
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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