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Record W4392607057 · doi:10.1016/j.gimo.2024.101066

P169: Characterizing the phenotypic spectrum and estimated penetrance of families with pathogenic loss of function variants in the FLT4 gene

2024· article· en· W4392607057 on OpenAlexaff
Olivia Moran, Cherith Somerville, Miriam S. Reuter, Kelsey Kalbfleisch, M. Schwartz, Xin Chen, Roozbeh Manshaei, Raymond H. Kim, Seema Mital, Rebekah Jobling

Bibliographic record

VenueGenetics in Medicine Open · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsPenetrancePhenotypeGeneticsLoss functionBiologyGeneFunction (biology)Computational biology

Abstract

fetched live from OpenAlex

Tetralogy of Fallot (TOF) is a complex congenital heart disease (CHD) that represents 5-10% of all CHD and is estimated to affect 1 in 3,000 newborns in the United States. TOF can present as part of a genetic syndrome or in isolation (non-syndromic). The genetic etiology of non-syndromic TOF is not well understood, however evidence supports an emerging role of heterozygous loss-of-function (LOF) variants in FLT4 (HGNC:3767). Recent evidence assigns a “strong” correlation between the FLT4 gene and TOF, based on ClinGen gene-disease validity criteria.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.269
Teacher spread0.254 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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