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Record W1754892555 · doi:10.1038/nrcardio.2015.161

Mitral valve disease—morphology and mechanisms

2015· review· en· W1754892555 on OpenAlexafffund
Robert A. Levine, Albert Hagège, Daniel P. Judge, Muralidhar Padala, Jacob P. Dal‐Bianco, Elena Aïkawa, Jonathan Beaudoin, Joyce Bischoff, Nabila Bouatia‐Naji, Patrick Bruneval, Jonathan T. Butcher, Alain Carpentier, Miguel Chaput, Adrian H. Chester, Catherine Clusel, Francesca N. Delling, Harry C. Dietz, Christian Dina, Ronen Durst, Leticia Fernández‐Friera, Mark D. Handschumacher, Morten Ø. Jensen, Xavier Jeunemaı̂tre, Hervé Le Marec, Thierry Le Tourneau, Roger R. Markwald, Jean Mérot, Emmanuel Messas, David Milan, Tui Néri, Russell A. Norris, David S. Peal, M. Perrocheau, Vincent Probst, Michael Pucéat, Nadia Rosenthal, Jorge Solı́s, Jean‐Jacques Schott, Ehud Schwammenthal, Susan A. Slaugenhaupt, Jae‐Kwan Song, Magdi H. Yacoub

Bibliographic record

VenueNature Reviews Cardiology · 2015
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité de Montréal
FundersNational Center for Research ResourcesNational Institute of General Medical SciencesAmerican Heart AssociationDirection Générale de l’offre de SoinsInstitut National de la Santé et de la Recherche MédicaleAgence Nationale de la RechercheSociété Française de CardiologieFondation LeducqEuropean CommissionHjerteforeningenNational Institutes of HealthEurostarsSociedad Española de CardiologíaNational Heart, Lung, and Blood InstituteHeart and Stroke Foundation of Canada
KeywordsMedicineCardiologyMitral valveMitral regurgitationInternal medicineHeart failureDiseaseMitral valve prolapseVentricular outflow tract obstructionCardiomyopathy

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.046
GPT teacher head0.424
Teacher spread0.378 · 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
GenreReview

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

Citations383
Published2015
Admission routes2
Has abstractno

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