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Record W4382197449 · doi:10.1002/ejhf.2944

Knowledge About Self-Efficacy and Outcomes in Patients with Heart Failure and Reduced Ejection Fraction

2023· article· en· W4382197449 on OpenAlexaff
Mingming Yang, Toru Kondo, Carly Adamson, Jawad H. Butt, William T. Abraham, Akshay S. Desai, Karola Jering, Lars Køber, Mikhail Kosiborod, Milton Packer, Jean L. Rouleau, Scott D. Solomon, Muthiah Vaduganathan, Michael R. Zile, Pardeep S. Jhund, John J.V. McMurray

Bibliographic record

VenueEuropean Journal of Heart Failure · 2023
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersChina Scholarship CouncilBritish Heart Foundation
KeywordsHeart failureMedicineEjection fractionInternal medicineCardiologyFraction (chemistry)Intensive care medicine

Abstract

fetched live from OpenAlex

AIM: Although education in self-management is thought to be an important aspect of the care of patients with heart failure, little is known about whether self-rated knowledge of self-management is associated with outcomes. The aim of this study was to assess the relationship between patient-reported knowledge of self-management and clinical outcomes in patients with heart failure and reduced ejection fraction (HFrEF). METHODS AND RESULTS: Using individual patient data from three recent clinical trials enrolling participants with HFrEF, we examined patient characteristics and clinical outcomes according to responses to the 'self-efficacy' questions of the Kansas City Cardiomyopathy Questionnaire. One question quantifies patients' understanding of how to prevent heart failure exacerbations ('prevention' question) and the other how to manage complications when they arise ('response' question). Self-reported answers from patients were pragmatically divided into: poor (do not understand at all, do not understand very well, somewhat understand), fair (mostly understand), and good (completely understand). Cox-proportional hazard models were used to evaluate time-to-first occurrence of each endpoint, and negative binomial regression analysis was performed to compare the composite of total (first and repeat) heart failure hospitalizations and cardiovascular death across the above-defined groups. Of patients (n = 17 629) completing the 'prevention' question, 4197 (23.8%), 6897 (39.1%), and 6535 (37.1%) patients had poor, fair, and good self-rated knowledge, respectively. Of those completing the 'response' question (n = 17 637), 4033 (22.9%), 5463 (31.0%), and 8141 (46.2%) patients had poor, fair, and good self-rated knowledge, respectively. For both questions, patients with 'poor' knowledge were older, more often female, and had a worse heart failure profile but similar treatment. The rates (95% confidence interval) per 100 person-years for the primary composite outcome for 'poor', 'moderate' and 'good' self-rated knowledge in answer to the 'prevention' question were 12.83 (12.11-13.60), 12.08 (11.53-12.65) and 11.55 (11.00-12.12), respectively, and for the 'response' question were 12.88 (12.13-13.67), 12.22 (11.60-12.86) and 11.56 (11.07-12.07), respectively. The lower event rates in patients with 'good' self-rate knowledge were accounted for by lower rates of cardiovascular (and all-cause) death and not hospitalization for worsening heart failure. CONCLUSIONS: Poor patient-reported 'self-efficacy' may be associated with higher rates of mortality. Evaluation of knowledge of 'self-efficacy' may provide prognostic information and a guide to which patients may benefit from further education about self-management.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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 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

Citations7
Published2023
Admission routes1
Has abstractyes

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