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Record W2768372523 · doi:10.1111/ctr.13158

The prognostic significance of frailty compared to peak oxygen consumption and B‐type natriuretic peptide in patients with advanced heart failure

2017· article· en· W2768372523 on OpenAlexaff
Yasbanoo Moayedi, Juan Duero Posada, Farid Foroutan, Lívia Adams Goldraich, Ana C. Alba‐Rubio, Jane MacIver, Heather J. Ross

Bibliographic record

VenueClinical Transplantation · 2017
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsTed Rogers Centre for Heart ResearchUniversity Health Network
Fundersnot available
KeywordsMedicineNatriuretic peptideHeart failureInternal medicineCardiologyHeart transplantationBrain natriuretic peptideClinical significance

Abstract

fetched live from OpenAlex

Abstract Frailty assessment has become an integral part of the evaluation of potential candidates for heart transplantation and ventricular assist device ( HT x/ VAD ). The impact of frailty, as a heart failure risk factor or to identify those who will derive the greatest benefit with HT x/ VAD remains unclear. The aim of this study was to evaluate the independent prognostic relevance of frailty assessment from peak oxygen consumption (peak VO 2 ) or B‐type natriuretic peptide ( BNP ) on mortality in patients referred for advanced heart failure therapies. Frailty was measured using modified Fried frailty criteria. In 201 consecutive patients, during a median follow‐up of 17.5 months ( IQR 11‐29.2), there were 25 (12.4%) deaths. One‐year survival was 100%, 94%, and 78% in nonfrail, prefrail, and frail patients, respectively (log rank P = .0001). Frailty was associated with a twofold increase risk of death ( HR 2.01, P < .0001, 95% CI 1.42‐2.84). When adjusted for BNP or peak VO 2 , frailty was not associated with a significant risk of all‐cause death. However, when peak VO 2 is stratified into two categories (≥12 mL/kg/min vs <12 mL/kg/min), frailty was associated with increased mortality in patients with a lower peak VO 2 ( HR 1.72, P = .006).

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)0.001
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 score0.003
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.046
GPT teacher head0.355
Teacher spread0.308 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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