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Record W4212824682 · doi:10.1111/jocs.16325

Valve deterioration: A victim of construct over time?

2022· article· en· W4212824682 on OpenAlexaff
Ali Fatehi Hassanabad, Muhammad Rauf Ahsan, Makoto Hibino

Bibliographic record

VenueJournal of Cardiac Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsLibin Cardiovascular Institute of Alberta
Fundersnot available
KeywordsMedicineHeart failureHeart valveAortic valve replacementAortic valveIncidence (geometry)Heart valve replacementCardiologyInternal medicineSurgeryValve replacementStenosis

Abstract

fetched live from OpenAlex

Surgical aortic valve replacement (sAVR) remains one of the most common cardiac operations performed globally on an annual basis. Biological and mechanical valves comprise the two classes of prosthetic valves available to surgeons. Biological prosthetic valves can be prone to failure and structural valve deterioration (SVD), which may necessitate reintervention. Recent literature suggests that the Trifecta heart valve is susceptible to early failure. In this retrospective study, Yount et al. use institutional data to assess the longevity of the Trifecta heart valve. The investigators included patients who had undergone sAVR and had received either a Trifecta prosthetic heart valve or a Magna/Magna Ease heart valve. While there were some baseline differences between the patient groups, the study found that those who had received a Trifecta valve had higher rates of valve failure. This is an important study that adds valuable evidence pertaining to the incidence of failure and SVD with the Trifecta heart valve. Although further studies may shed light on the precise mechanisms that drive valve failure and deterioration, surgeons should be aware of the mounting clinical data in this area.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.004
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)0.0010.000

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 head0.011
GPT teacher head0.294
Teacher spread0.283 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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