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Record W4404266099 · doi:10.4244/eij-e-24-00054

TAVI patients with bystander coronary artery disease should receive PCI: pros and cons

2024· article· en· W4404266099 on OpenAlexaff
Josep Rodés-Cabau, Marisa Avvedimento, Benedict McDonaugh, Tiffany Patterson

Bibliographic record

VenueEuroIntervention · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineBystander effectconsConventional PCICoronary artery diseaseCardiologyInternal medicineCoronary diseaseCoronary heart diseaseMyocardial infarction

Abstract

fetched live from OpenAlex

The coexistence of coronary artery disease (CAD) and severe aortic stenosis is a frequent and complex clinical scenario, affecting up to 80% of patients undergoing transcatheter aortic valve implantation (TAVI). As the profile of TAVI candidates evolves − with younger patients and longer life expectancies − the prognostic implications of CAD become increasingly relevant. In addition, while aortic stenosis is a life-threatening condition requiring treatment to reduce overall mortality, the management of concomitant CAD is also crucial to improve symptoms. Treating significant CAD may undermine the benefits of TAVI, leading to persistent angina, adverse cardiac events over time, and reduced quality of life. However, stable CAD rarely necessitates urgent intervention, and percutaneous coronary intervention (PCI) carries potential risks (including bleeding, stroke, and acute kidney injury) without clear benefits in this setting. Although two randomised trials investigated this delicate issue, whether treating bystander CAD in patients undergoing TAVI is associated with favourable prognostic implications or merely adds procedural risks remains a matter of debate.

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 score0.005
Threshold uncertainty score0.404

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 head0.021
GPT teacher head0.319
Teacher spread0.299 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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