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Risk factors for recurrence of aortic regurgitation after aortic valve repair

2021· review· en· W3086681958 on OpenAlexaff
Vincent Chauvette, Elbert E. Williams, Ismaı̈l El-Hamamsy

Bibliographic record

VenueThe Journal of Cardiovascular Surgery · 2021
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsRegurgitation (circulation)MedicineAortic valve replacementCardiologyInternal medicineAortic valveSurgeryStenosis

Abstract

fetched live from OpenAlex

In the past decade, the treatment of aortic regurgitation has evolved from a strategy focused mainly on valve replacement to one of valve repair (AVr). Nevertheless, AVr has yet to gain broad acceptance within the surgical community despite excellent reported results in selected centers. In fact, AVr failure due to recurrence of regurgitation remains a challenge. Thanks to sustained research efforts, the mechanisms underlying aortic valve repair failure are now better understood. The aim of this article is to discuss the risk factors for recurrent AR after valve repair, the technical strategies that can mitigate this risk and highlight the contemporary results associated with AVr.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-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 score0.838
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.076
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.346
Teacher spread0.307 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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