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Record W4403524856 · doi:10.1038/s41431-024-01702-y

Epigenomic and phenotypic characterization of DEGCAGS syndrome

2024· article· en· W4403524856 on OpenAlexafffund
Karim Karimi, Denisa Weis, Ingvild Aukrust, Tzung‐Chien Hsieh, Julie Paulsen, Roberto Mendoza‐Londono, Lucie Dupuis, Hellen Lesmann, Tracy Lau, David Murphy, Khalid Hama Salih, Bassam Musa Sadik Al-Musawi, Ruqayah G. Y. Al‐Obaidi, Małgorzata Rydzanicz, Mateusz Biela, Michael S. Santos, Abdulrahman Aldeeri, Hanna T. Gazda, Lynn Pais, Shirlee Shril, Henrik Døllner, Sandip Bartakke, Franco Laccone, Andrea Šoltýsová, Thomas M. Kitzler, Neveen A. Soliman, Raissa Relator, Michael A. Levy, Jennifer Kerkhof, Jessica Rzasa, Henry Houlden, Gabriela V. Pilshofer, Tilman Jobst‐Schwan, Friedhelm Hildebrandt, Sérgio B. Sousa, Reza Maroofian, Timothy W. Yu, Peter Krawitz, Bekim Sadiković, Sofia Douzgou

Bibliographic record

VenueEuropean Journal of Human Genetics · 2024
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsWestern UniversityMcGill UniversityHospital for Sick ChildrenLondon Health Sciences Centre
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Diabetes and Digestive and Kidney DiseasesNational Human Genome Research InstituteDeutsche ForschungsgemeinschaftOntario Genomics InstituteNational Institutes of HealthU.S. Department of Health and Human ServicesGovernment of CanadaBroad InstituteOntario GenomicsGenome Canada
KeywordsEpigenomicsPhenotypeGeneticsBiologyComputational biologyDNA methylationGene

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.245
Teacher spread0.228 · 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

Citations5
Published2024
Admission routes2
Has abstractno

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