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Record W4394569714 · doi:10.1016/j.jacadv.2024.100877

TAVR in Older Adults

2024· article· en· W4394569714 on OpenAlexaff
Abdulla A. Damluji, Gwen M. Bernacki, Jonathan Afilalo, Radmila Lyubarova, Ariela R. Orkaby, Min Ji Kwak, Scott L. Hummel, James N. Kirkpatrick, Matthew J. Maurer, Nanette K. Wenger, Michael W. Rich, Dae Hyun Kim, Roberta Y. Wang, Daniel E. Forman, Ashok Krishnaswami

Bibliographic record

VenueJACC Advances · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsMcGill University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institute on AgingPatient-Centered Outcomes Research InstituteU.S. Department of Veterans Affairs
KeywordsRisk stratificationMedicineStenosisValve replacementOlder peopleIntensive care medicineCardiologyInternal medicineGerontology

Abstract

fetched live from OpenAlex

Calcific aortic stenosis can be considered a model for geriatric cardiovascular conditions due to a confluence of factors. The remarkable technological development of transcatheter aortic valve replacement was studied initially on older adult populations with prohibitive or high-risk for surgical valve replacement. Through these trials, the cardiovascular community has recognized that stratification of these chronologically older adults can be improved incrementally by invoking the concept of frailty and other geriatric risks. Given the complexity of the aging process, stratification by chronological age should only be the initial step but is no longer sufficient to optimally quantify cardiovascular and noncardiovascular risk. In this review, we employ a geriatric cardiology lens to focus on the diagnosis and the comprehensive management of aortic stenosis in older adults to enhance shared decision-making with patients and their families and optimize patient-centered outcomes. Finally, we highlight knowledge gaps that are critical for future areas of study.

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)0.003
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.352
Teacher spread0.347 · 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

Citations36
Published2024
Admission routes1
Has abstractyes

Explore more

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