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Record W4408531621 · doi:10.1016/j.gimo.2025.102864

P020: Introducing an interactive, searchable database of LC-FAOD gene variants, genotypes and phenotypes

2025· article· en· W4408531621 on OpenAlexaff
Vanessa Rangel Miller, Omid Japalaghi, Heather A. Richbourg, Moeenaldeen AlSayed, Peter R. Baker, Sarah C. Grünert, Sean C. Daugherty, Tali Ekstein, Mark J. Kiel, Hironori Kobayashi, Lawrence Korngut, Stephanie Monteleone, Ida Vanessa Döederlein Schwartz, Nicole L. Miller, Jerry Vockley

Bibliographic record

VenueGenetics in Medicine Open · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPhenotypeGenotypeGeneGeneticsGenotype-phenotype distinctionBiologyDatabaseComputational biologyBioinformaticsComputer science

Abstract

fetched live from OpenAlex

Long-chain fatty acid oxidation disorders (LC-FAOD) are rare, life-threatening, treatable conditions detected through acylcarnitine profile analysis, performed through newborn screening (NBS) or clinical presentation when NBS is not available or missed. Confirming a LC-FAOD diagnosis with genetic testing is complicated by the rarity of the disorders, genetic and phenotypic heterogeneity, and high frequency of variants of uncertain significance (VUS). A centralized repository of LC-FAOD gene variant data aims to improve access to information needed for variant classification and clinical diagnosis.

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)0.015
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: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0480.015

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 head0.027
GPT teacher head0.361
Teacher spread0.333 · 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
GenreSoftware

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

Citations0
Published2025
Admission routes1
Has abstractyes

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