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Record W4416933312 · doi:10.21037/acs-2025-mac-0136

Tips and tricks in addressing mitral annular calcification in mitral valve surgery

2025· review· en· W4416933312 on OpenAlexaff
Malak Elbatarny, Tirone E. David

Bibliographic record

VenueAnnals of Cardiothoracic Surgery · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsContext (archaeology)CalcificationPercutaneousMitral valveMitral valve replacementValve replacementAortic valve

Abstract

fetched live from OpenAlex

Mitral annular calcification (MAC) is a heterogeneous condition that can complicate mitral valve (MV) repair and replacement operations. Surgery in the context of MAC varies considerably. Relatively simple repairs or replacements can be performed in several patients without the need to remove the calcium bar. However, extensive annular debridement, reconstruction of the atrioventricular junction, reconstruction of the intervalvular fibrous body, and mitral and aortic valve replacement may be necessary in some instances. Outcomes of these patients are directly related to the avoidance of technical problems amid a variety of potential anatomic challenges and pitfalls. Careful preoperative assessment, patient selection, detailed preoperative planning, and intraoperative judgment are required to optimize the chance of a successful outcome. Here we describe our approach to the MV patients with MAC, including preoperative planning, intraoperative technical tips and tricks, as well as a discussion of outcomes and remaining questions in this challenging population. This review only includes patients with MAC and MV dysfunction. It excludes those with associated aortic valve disease. The article contains a lecture on MV surgery in patients with MAC. We also highlight emerging experimental approaches, including hybrid and percutaneous techniques.

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)0.002
metaresearch head score (Gemma)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.778
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.014
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.216
GPT teacher head0.496
Teacher spread0.280 · 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 designOther design
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

Citations0
Published2025
Admission routes1
Has abstractyes

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