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Low-Dose vs Standard Warfarin After Mechanical Mitral Valve Replacement: A Randomized Trial

2023· article· en· W4313493351 on OpenAlexaff
Michael Chu, Marc Ruel, Allen Graeve, Marc Gerdisch, Ralph J. Damiano, Robert L. Smith, W. Brent Keeling, Michael A. Wait, Robert Hagberg, Reed D. Quinn, Gulshan K. Sethi, Rosario Floridia, Christopher J. Barreiro, Andrew L. Pruitt, Kevin Accola, François Dagenais, Alan Markowitz, Jian Ye, Michael Sekela, Ryan Tsuda, David A Duncan, Daniel G. Swistel, Lacy E. Harville, Joseph J. DeRose, Eric J. Lehr, John H. Alexander, John D. Puskas, Chun “Dan” Choi, Gösta Pettersson, O.H. Frazier, Jeffrey Askew, David Duncan, Romualdo J. Segurola, Mohammad Shoukfeh, Igor D. Gregorič, Steven Meyer, Danny Chu, Eric Kirker, Lance Landvater, Brian Castlemain, Peter Tutuska, Thomas M. Beaver, Alan Graeve, David Liu, Bryan A. Whitson, Lacy Harville, Christian Shults, Prem Shekar, Vinay Badhwar

Bibliographic record

VenueThe Annals of Thoracic Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of OttawaLondon Health Sciences CentreVancouver General HospitalInstitut universitaire de cardiologie et de pneumologie de QuébecWestern University
FundersNational Institutes of HealthCryoLifeCSL BehringAbbott VascularAtriCureU.S. Food and Drug AdministrationBristol-Myers SquibbEdwards LifesciencesBayerPfizer
KeywordsMedicineWarfarinClinical endpointRandomized controlled trialMitral valve replacementSurgeryAspirinThrombosisMitral valveCardiologyInternal medicineAtrial fibrillation

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0120.002

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 head0.074
GPT teacher head0.422
Teacher spread0.349 · 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 designRandomized trial
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

Citations38
Published2023
Admission routes1
Has abstractno

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