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Record W4407636660 · doi:10.1542/peds.2024-070077

Guidance for the Primary Care Provider in Identifying Infants With Biliary Atresia by 2–4 Weeks of Life: Clinical Report

2025· article· en· W4407636660 on OpenAlexaff
Sanjiv Harpavat, Susan W. Aucott, Saul J. Karpen, Benjamin L. Shneider, Kasper Wang, Mitchell B. Cohen, David Brumbaugh, Jennifer L. Dotson, Jenifer R. Lightdale, Daniel Mallon, Maria Oliva‐Hemker, Eric C. Eichenwald, Charletta Guillory, Ivan Hand, Mark L. Hudak, David A. Kaufman, Camilia R. Martin, Margaret Parker, Kelly C. Wade, Andrew M. Davidoff, Elizabeth A. Beierle, Gail E. Besner, Marybeth Browne, Cynthia D. Downard, Kenneth W. Gow, Saleem Islam, Danielle Walsh

Bibliographic record

VenuePEDIATRICS · 2025
Typearticle
Languageen
FieldMedicine
TopicPediatric Hepatobiliary Diseases and Treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineBiliary atresiaPediatricsPrimary careJaundiceIntensive care medicineLiver transplantationFamily medicineInternal medicineTransplantation

Abstract

fetched live from OpenAlex

This report helps pediatric primary care providers quickly identify infants with biliary atresia, which has the potential to improve outcomes and reduce need for liver transplant. The strategy is intended to be used between 2 and 4 weeks of life at the "By 1 month" well-child visit in the Bright Futures/American Academy of Pediatrics "Recommendations for Preventive Pediatric Health Care." The strategy involves examining every infant's eye color, stool color, and prior laboratory results to determine whether measurement of a direct or conjugated bilirubin level is warranted.

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)0.002
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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0120.006

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 head0.021
GPT teacher head0.336
Teacher spread0.315 · 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 designCase report
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

Citations7
Published2025
Admission routes1
Has abstractyes

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