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Record W4407065139 · doi:10.1016/j.ymgme.2025.109048

The natural history of variable subtypes in pediatric-onset TUBB4A-related leukodystrophy

2025· article· en· W4407065139 on OpenAlexafffund
Francesco Gavazzi, Brittany A. Charsar, Eline M. Hamilton, Jacqueline Erler, Virali Patel, Sarah Woidill, Anjana Sevagamoorthy, Guy Helman, Johanna Schmidt, Amy Pizzino, Kayla Muirhead, Asako Takanohashi, Joshua L. Bonkowsky, Kelsee Meyerhoffer, Cas Simons, Hiroshi Doi, Miyatake Satoko, Naomichi Matsumoto, Mauricio R. Delgado, Meredith Sanchez‐Castillo, Daniel R. Carvalho, Ivailo Tournev, Teodora Chamova, Albena Jordanova, Nancy J. Clegg, Francesco Nicita, Enrico Bertini, Michelle Teng, Dan Williams, Davide Tonduti, Henry Houlden, Menno D. Stellingwerff, Evangeline Wassmer, Ángeles García‐Cazorla, Geneviève Bernard, Amytice Mirchi, Helia Toutounchi, Nicole I. Wolf, Marjo S. van der Knaap, Justine Shults, Laura Adang, Adeline Vanderver

Bibliographic record

VenueMolecular Genetics and Metabolism · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA regulation and disease
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchNational Institutes of HealthNational Center for Advancing Translational SciencesFonds de Recherche du Québec - SantéFoundation for the National Institutes of HealthBulgarian National Science FundFonds Wetenschappelijk OnderzoekFonds De La Recherche Scientifique - FNRSNational Institute of Neurological Disorders and StrokeAssociation Belge contre les Maladies Neuro-Musculaires
KeywordsLeukodystrophyNatural historyMedicinePediatricsInternal medicineDisease

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
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
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.003
GPT teacher head0.201
Teacher spread0.199 · 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

Citations9
Published2025
Admission routes2
Has abstractno

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