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Record W3048833652 · doi:10.1016/j.xjtc.2020.08.012

Papillary muscle relocation with a multiloop suture: A proposed surgical technique for ischemic mitral regurgitation

2020· article· en· W3048833652 on OpenAlexaff
Lawrence Torkan, Gianluigi Bisleri

Bibliographic record

VenueJTCVS Techniques · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsQueen's University
FundersKARL STORZMedtronic
KeywordsPapillary muscleMedicineCardiologyInternal medicineMitral regurgitationMitraClipIschemic cardiomyopathyFibrous jointFunctional mitral regurgitationMitral valve repairHeart failureEjection fractionSurgery

Abstract

fetched live from OpenAlex

Ischemic mitral regurgitation (IMR), a subtype of functional MR, occurs when mitral valve leaflets cannot adequately coapt in the absence of structural abnormalities. Functional MR is associated with increased morbidity and mortality.1 Ischemia causes left ventricular remodeling, annular dilatation, and poor leaflet coaptation. Treatment involves revascularization via coronary artery bypass grafting (CABG) and mitral valve repair via restrictive annuloplasty (RA). Recurrence of MR is still seen in up to 58.8% of patients at 2 years postoperatively.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.002

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 head0.016
GPT teacher head0.325
Teacher spread0.309 · 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
GenreMethods

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

Citations3
Published2020
Admission routes1
Has abstractyes

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