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Record W4210267903 · doi:10.1097/hco.0000000000000949

Supporting the Ross procedure: preserving root physiology while mitigating autograft dilatation

2022· article· en· W4210267903 on OpenAlexaff

Bibliographic record

VenueCurrent Opinion in Cardiology · 2022
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAortic rootRoss procedureDynamismDiseaseAortic valve

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The purpose of this article is to describe the optimized approach to nonrepairable aortic valve disease in young adults with a Ross procedure, while preserving the dynamic physiology of the aortic root. RECENT FINDINGS: As the techniques for supporting pulmonary autografts continue to be refined, and the applicability of the Ross procedure continues to expand, an assessment of the various techniques based on aortic root physiology is warranted. Semi-resorbable scaffolds show promise in ovine models for improving the Ross procedure. Recent long-term outcomes for the Dacron inclusion technique in comparison to more physiologic methods of support emphasize the importance of balancing the prevention of early dilatation with the preservation of root haemodynamics. As this review will synthesize, the dynamic physiology of the root may be preserved even in patients at a higher risk of autograft dilatation. SUMMARY: The favourable long-term outcomes of the Ross procedure can be partly attributed to the ability of the autograft to restore dynamism to the neoaortic root. Patient-specific modifications that respect root physiology can tailor the Ross procedure to address each patient's risk factors for early dilatation and late failure. As such, the Ross procedure should be recognized as an increasingly favourable solution for a wide spectrum of nonpreservable aortic valve disease in young adults.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.066
GPT teacher head0.366
Teacher spread0.300 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations12
Published2022
Admission routes1
Has abstractyes

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