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Record W3004236744 · doi:10.1016/j.cjco.2019.12.006

Effects of Transcatheter Aortic Valve Implantation on Frailty and Quality of Life

2020· article· en· W3004236744 on OpenAlexaff
Pishoy Gouda, Chai Paterson, Steven Meyer, Miriam Shanks, Craig Butler, Dylan Taylor, Benjamin D. Tyrrell, Robert C. Welsh

Bibliographic record

VenueCJC Open · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsCanadian Heart Research CentreCanadian VIGOUR CentreUniversity of Alberta
Fundersnot available
KeywordsMedicineQuality of life (healthcare)StenosisPopulationPhysical therapyCardiology

Abstract

fetched live from OpenAlex

BACKGROUND: Transcatheter aortic valve implantation (TAVI) is an effective alternative to surgical valve replacement in high-risk patients with severe aortic stenosis. Although measures of frailty have been used to attempt to predict outcomes in this population, few studies have demonstrated changes in these measures. METHODS: We performed a prospective, observational study of 171 patients undergoing TAVI, of whom 44 had maximal follow-up of 1 month and 50 had maximal follow-up of 1 year. Quality of life was assessed using the Minnesota Living With Heart Failure Questionnaire, Katz Index of Independence in Activities of Daily Living questionnaire, and patient perception of overall well-being. Frailty was measured using the 10-m walk test and handgrip strength testing. RESULTS: In the overall cohort, participants demonstrated improvements in quality of life metrics, but deterioration in 10-m walk test and handgrip at 1 month. These trends continued at 1 year. However, patients in the lowest quintile of handgrip and 10-m walk test demonstrated a trend of improvements in these metrics during follow-up. CONCLUSIONS: Despite improvements in quality of life after TAVI, no improvements in frailty were observed in patients at 1 year.

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 score0.051
Threshold uncertainty score0.185

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 head0.047
GPT teacher head0.391
Teacher spread0.345 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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