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Record W3182311085 · doi:10.21037/acs-2021-rp-25

The Ross procedure is an excellent operation in non-repairable aortic regurgitation: insights and techniques

2021· review· en· W3182311085 on OpenAlexaff
Amine Mazine, Ismaı̈l El-Hamamsy

Bibliographic record

VenueAnnals of Cardiothoracic Surgery · 2021
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineRegurgitation (circulation)Ross procedureStenosisCardiologyPulmonary regurgitationPopulationInternal medicineArgument (complex analysis)SurgeryAortic valve replacementHeart disease

Abstract

fetched live from OpenAlex

The Ross procedure is the best operation to treat aortic stenosis (AS) in young and middle-aged adults. However, its role in non-repairable aortic regurgitation (AR) remains debated since many historical series have reported an increased risk of pulmonary autograft dilatation and subsequent need for reintervention in these patients. Some have attributed these findings to an unrecognized and poorly characterized inherited genetic defect that prevents adaptive remodelling of the pulmonary autograft. Herein, we review the contemporary evidence surrounding the use of the Ross procedure in young adults with AR and put forth the argument that with proper technical refinements, the Ross procedure may still be the best operation to treat these patients. We believe that by tailoring the operation to the patient's anatomy and ensuring strict postoperative blood pressure control, one can achieve excellent results with the Ross procedure, including in this challenging patient population.

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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.094
GPT teacher head0.454
Teacher spread0.360 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

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