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Record W4410369255 · doi:10.1097/crd.0000000000000951

Minimally Invasive Tricuspid Valve Surgery: An Alternative Surgical Approach in the Era of Transcatheter Interventions

2025· article· en· W4410369255 on OpenAlexaff
Ali Fatehi Hassanabad, Angela Kim, Daniyil A. Svystonyuk, Gianluigi Bisleri, Corey Adams, William Kent

Bibliographic record

VenueCardiology in Review · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of TorontoMcGill UniversityLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineTricuspid valveInvasive surgerySurgeryBlood lossValve replacementCardiology

Abstract

fetched live from OpenAlex

There has been significant growth in minimally invasive valve surgery over the past 2 decades, with novel approaches including video-assisted minithoracotomy, totally endoscopic, and robotic assisted. Outcomes of these techniques suggest that they can improve patient-reported outcomes, enhance mobility, reduce blood loss, and facilitate earlier discharge and return to work. Minimally invasive tricuspid valve surgery now provides surgeons and patients with an alternative option for treating tricuspid valve disease. These operations can be conducted safely and yield favorable outcomes with carefully selected patients. Herein, we provide a comprehensive overview of minimally invasive tricuspid valve surgery, including a summary of the current literature on minithoracotomy valve repair or replacement, beating heart, redo, and multiple-valve surgery. Finally, we contextualize how minimally invasive tricuspid valve surgery can play an important role in the era of emerging transcatheter strategies.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.055
GPT teacher head0.396
Teacher spread0.341 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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