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Record W2266573952 · doi:10.1038/ng.3383

Genetic association analyses highlight biological pathways underlying mitral valve prolapse

2015· article· en· W2266573952 on OpenAlexafffund
Christian Dina, Nabila Bouatia‐Naji, Nathan R. Tucker, Francesca N. Delling, Katelynn Toomer, Ronen Durst, M. Perrocheau, Leticia Fernández‐Friera, Jorge Solı́s, Thierry Le Tourneau, Ming‐Huei Chen, Vincent Probst, Yohan Bossé, Philippe Pîbarot, Diana Zélénika, Mark Lathrop, Serge Herçberg, Ronan Roussel, Emelia J. Benjamin, Fabrice Bonnet, Su Hao Lo, Elena Dolmatova, Floriane Simonet, Simon Lecointe, Florence Kyndt, Richard Redon, Hervé Le Marec, Philippe Froguel, Patrick T. Ellinor, Ramachandran S. Vasan, Patrick Bruneval, Roger R. Markwald, Russell A. Norris, David J. Milan, Susan Slaugenhaupt, Robert A. Levine, Jean‐Jacques Schott, Albert Hagège, Xavier Jeunemaı̂tre

Bibliographic record

VenueNature Genetics · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité LavalGenome CanadaInstitut universitaire de cardiologie et de pneumologie de Québec
FundersNational Center for Research ResourcesNational Heart, Lung, and Blood InstituteFonds de Recherche du Québec - SantéAmerican Heart AssociationCanadian Institutes of Health ResearchAgence Nationale de la RechercheFondation LeducqEuropean CommissionNational Institutes of HealthSociedad Española de CardiologíaMassachusetts General HospitalNational Institute of General Medical Sciences
KeywordsBiologyMitral valve prolapseZebrafishTensinGenome-wide association studyCandidate geneGeneticsGenetic associationPhenotypeInternal medicineMitral valveGeneBioinformaticsSingle-nucleotide polymorphismMedicinePTENSignal transductionGenotypePI3K/AKT/mTOR pathway

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.093
GPT teacher head0.397
Teacher spread0.305 · 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 teacher head, 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

Citations135
Published2015
Admission routes2
Has abstractno

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