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Condition-Specific Growth Charts for Children With Alagille Syndrome

2025· article· en· W4416591177 on OpenAlexafffund
Koen Huysentruyt, Shannon M. Vandriel, Mathieu Roelants, David A. Piccoli, Kathleen M. Loomes, Elizabeth B. Rand, Noelle H. Ebel, Jeffrey A. Feinstein, Irena Jankowska, Piotr Czubkowski, Dorota Gliwicz‐Miedzińska, Emmanuel Gonzalès, Emmanuel Jacquemin, Jérôme Bouligand, Saul J. Karpen, René Romero, Henry C. Lin, Björn Fischler, Henrik Arnell, Liting Li, Jian‐She Wang, Rima Fawaz, Silvia Nastasio, Kyung Mo Kim, Seak Hee Oh, Lorenzo D’Antiga, Emanuele Nicastro, Ryan T. Fischer, Susan Siew, Michael Stormon, Chatmanee Lertudomphonwanit, Winita Hardikar, Sahana Shankar, James E. Squires, Shikha S. Sundaram, Catherine Larson‐Nath, Déirdre Kelly, Jane Hartley, Pınar Bulut, M. Kyle Jensen, Catalina Jaramillo, Amin J. Roberts, Helen Evans, Étienne Sokal, Tanguy Demaret, Henkjan J. Verkade, Richard J. Thompson, Bettina E. Hansen, Tim Cole, Binita M. Kamath, Dominique Debray, Florence Lacaille, Jernej Brecelj, Nehal El‐Koofy, Mohamed A. Elmonem, Way Seah Lee, Maria Camila Sanchez, Maria Lorena Cavalieri, Christina Hajinicolaou, Kathleen B. Schwarz, Elisa de Carvalho, Nathalie Rock, Wikrom Karnsakul, Rubén E. Quirós‐Tejeira, Seema Alam, Gabriella Nebbia, Yael Mozer‐Glassberg, Pamela L. Valentino, Ermelinda Santos Silva, Zerrin Önal, Antal Dezsőfi‐Gottl, Melina Melere, María Legarda Tamara, John Eshun, Aglaia Zellos, Giuseppe Indolfi, Maria Rogalidou, Niviann Blondet, Pier Luigi Calvo, Marisa Beretta, Andréanne N. Zizzo, Çiğdem Arıkan, Mureo Kasahara, Nanda Kerkar, Amal Aqul, Victorien M. Wolters, Raquel Borges Pinto, Jennifer García, Sabina Więcek, Christos Tzivinikos, Quais Mujawar, Carolina Jiménez‐Rivera, Cristina Molera Busoms, Cristina Gonçalves, Luís Bujanda

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicPediatric Hepatobiliary Diseases and Treatments
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of ManitobaLondon Health Sciences CentreWestern UniversityChildren's Hospital of Western OntarioUniversity of TorontoUniversity Health NetworkToronto General HospitalInstitute of Health Services and Policy ResearchHospital for Sick Children
FundersGrifolsIpsenHospital for Sick ChildrenNational Natural Science Foundation of ChinaSarepta TherapeuticsCSL BehringNational Institute of Diabetes and Digestive and Kidney DiseasesFudan UniversityAlnylam PharmaceuticalsGilead Sciences
KeywordsAlagille syndromeGrowth chartMEDLINEGrowth retardationCongenital disease

Abstract

fetched live from OpenAlex

Importance: Different degrees of growth delay have been reported in children with Alagille syndrome (ALGS), yet these patients are routinely evaluated using standard growth charts. Objective: To develop condition-specific growth charts for ALGS using modern statistical approaches. Design, Setting, and Participants: This case series used data from the international, multicenter Global Alagille Alliance (GALA) study accrued between May 14, 2018, and March 20, 2023. Children born at full term between January 1, 1997, and August 31, 2019, with a clinically and/or genetically confirmed ALGS diagnosis and their native liver were included. Data from children with a known history of prematurity were excluded for the development of the growth charts. Data were analyzed from March 25, 2023, to December 30, 2024. Exposure: Growth of children with Alagille syndrome. Main Outcomes and Measures: Generalized additive models for location scale and shape were fitted to generate percentile plots for weight and height relative to age and superimposed on US Centers for Disease Control and Prevention (CDC) growth charts to illustrate differences in growth patterns compared with children with typical development. Results: Data from 1204 children with ALGS in overlapping cohorts (median [IQR] gestational age, 38 [37-39] weeks) were analyzed (1204 in the weight cohort; 695 boys [57.7%]; 9855 weight observations; 995 with neonatal cholestasis [82.6%]; 306 receiving a liver transplant [25.4%]; 98 deaths [8.1%] and 1106 in the height cohort, 635 boys [57.4%]; 8464 height observations; 906 with neonatal cholestasis [81.9%]; 287 receiving a liver transplant [25.9%]; 86 deaths [7.8%]) were included for the modeling of the weight-for-age and height-for-age charts, respectively. The median birth weight was 2.8 kg (IQR, 2.5-3.0 kg) for boys and 2.6 kg (IQR, 2.4-2.9 kg) for girls. The median birth length was 48.0 cm (IQR, 46.0-50.0 cm) for boys and 47.0 cm (IQR, 45.0-49.0 cm) for girls. The weight-for-age and height-for-age growth charts for boys and girls with AGLS differed significantly from CDC growth charts. The estimated height at age 18 years corresponded to the 50th percentile was 171.5 cm for boys and 156.5 cm for girls on the condition-specific charts vs 176 cm and 163 cm, respectively, on the CDC growth charts. Conclusions and Relevance: These findings suggest that condition-specific growth charts for ALGS may provide a crucial tool for clinicians to evaluate growth and aid in decision-making around listing children for liver transplant.

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)0.008
metaresearch head score (Gemma)0.033
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 score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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".

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Citations1
Published2025
Admission routes2
Has abstractyes

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