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Record W2322052451 · doi:10.1097/hco.0000000000000045

Mitral valve interventions in heart failure

2014· review· en· W2322052451 on OpenAlexaff
Talal Al‐Atassi, Tarek Malas, Thierry Mesana, Vincent Chan

Bibliographic record

VenueCurrent Opinion in Cardiology · 2014
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineMitral regurgitationHeart failurePercutaneousCardiologyMitral valve repairInternal medicineMitral valvePopulationRegurgitation (circulation)Surgery

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review describes new developments in mitral valve interventions for patients with heart failure. The review also discusses innovative therapies in the treatment of mitral regurgitation in patients with heart failure and describes novel risk factors of recurrent mitral regurgitation following repair in this population. RECENT FINDINGS: Percutaneous strategies are rapidly emerging as an important adjunct to conventional mitral surgery, specially for patients with functional mitral regurgitation and heart failure. Percutaneous therapies are a well-tolerated alternative to surgery in high-risk patients, but their long-term efficacy is not established. Partial ring annuloplasty and preoperative galectin-3 levels may be associated with recurrent mitral regurgitation after repair. Preclinical work has focused on injectable substances, which may ameliorate adverse left ventricular remodeling and recurrent mitral regurgitation after surgery. SUMMARY: Percutaneous therapies will continue to evolve and serve as an alternative to conventional surgery for patients with mitral regurgitation and heart failure. Determining anatomic and biochemical risk factors associated with recurrent mitral regurgitation after repair is crucial in tailoring therapy to individual patients. Preclinical work regarding infarct stabilization may benefit future patients with heart failure.

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 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 categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.015
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.129
GPT teacher head0.494
Teacher spread0.366 · 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.

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

Citations4
Published2014
Admission routes1
Has abstractyes

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