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Record W4408379705 · doi:10.1093/eurjpc/zwad400

Quality of life in heart failure. The heart of the matter. A scientific statement of the Heart Failure Association and the European Association of Preventive Cardiology of the European Society of Cardiology

2023· article· en· W4408379705 on OpenAlexaff
Maurizio Volterrani, Géza Hálasz, Stamatis Adamopoulos, Piergiuseppe Agostoni, Javed Butler, Andrew J.S. Coats, Alain Cohen‐Solal, Wolfram Doehner, Ewa A. Jankowska, Carolyn S.P. Lam, Ekaterini Lambrinou, Lars H. Lund, Giuseppe Rosano, Marco Metra, Stefania Paolillo, Pasquale Perrone Filardi, Amina Rakisheva, Gianluigi Savarese, Petar Seferović, Carlo G. Tocchetti, Massimo Piepoli

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2023
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Heart failurePopulationIntensive care medicineClinical trialDepression (economics)Physical therapyInternal medicineNursing

Abstract

fetched live from OpenAlex

For most patients with chronic, progressive illnesses, maintaining good quality of life (QoL), with preserved functional capacity, is just as crucial as prolonging survival. Patients with heart failure (HF) experience much worse QoL and effort intolerance than both the general population and people with other chronic conditions, since they present a range of physical and psychological symptoms, including shortness of breath, chest discomfort, fatigue, fluid congestion, trouble with sleeping, and depression. These symptoms reduce patients' capacity for daily social and physical activity. Usual endpoints of large-scale trials in chronic HF have mostly been defined to evaluate treatments regarding hospitalizations and mortality, but more recently, patients' priorities and needs expressed with QoL are gaining more awareness and are being more extensively evaluated. This scientific statement aims at discussing the importance of QoL in HF, summarizing the most largely adopted questionnaires in HF care, and providing an overview on their application in trials and the potential for their transition to clinical practice. Finally, by discussing the reasons limiting their application in daily clinical routine and the strategies that may promote their implementation, this statement aims at fostering the systematic integration of the patient's standpoint in HF care.

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)0.032
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0320.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.022
GPT teacher head0.279
Teacher spread0.258 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

Explore more

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