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Record W4416320033 · doi:10.1590/ce.v30i0.99574en

Prevalence and factors associated with anxiety and depressive symptoms in patients with heart failure

2025· article· W4416320033 on OpenAlexaff
Danielly Farias Santos de Lima, Juliana Pessoa de Souza, Lidiane Lima de Andrade, Oriana Deyze Correia Paiva Leadebal, Maria Eliane Moreira Freire, Suzanne Fredericks, Mailson Marques de Sousa

Bibliographic record

VenueCogitare Enfermagem · 2025
Typearticle
Language
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAnxietyDepression (economics)Heart failureMarital statusOutpatient clinicPsychological interventionQuality of life (healthcare)Depressive symptoms

Abstract

fetched live from OpenAlex

ABSTRACT Objective: To identify the prevalence and sociodemographic and clinical factors associated with anxiety and depressive symptoms in patients with heart failure at a cardiology outpatient clinic. Methods: A cross-sectional study was conducted at a cardiology outpatient clinic in João Pessoa, Paraíba, Brazil, involving 88 patients. Anxiety and depressive symptoms were assessed using the Hospital Anxiety and Depression Scale. Association tests, Spearman’s correlation and Poisson regression were conducted. Results: The prevalence of anxiety symptoms was 67.1%, and the prevalence of depressive symptoms was 34.1%. Marital status, sex, and education level were significantly associated with anxiety symptoms. Conclusions: A high prevalence of anxiety and depression symptoms was identified. Health interventions are necessary to minimize the impact of psychological symptoms, as such measures are essential to improve adherence to therapy and the quality of life of patients with heart failure.

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)0.002
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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