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Health-Related Quality of Life in Women with Coronary Artery Disease

2008· article· en· W1996733308 on OpenAlexaffabout
Hallveig Broddadottir, Louise Jensen, Colleen M. Norris, Michelle M. Graham

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineCoronary artery diseaseQuality of life (healthcare)Coronary angiographyInternal medicineDepression (economics)Physical therapyDiseaseRevascularizationCardiac catheterizationCardiologyMyocardial infarction

Abstract

fetched live from OpenAlex

BACKGROUND: Women with coronary artery disease (CAD) have reported worse health-related quality of life (HRQOL) than men. OBJECTIVES: The purpose of this study was to explore HRQOL in women with CAD undergoing coronary angiography. Specifically, the effects of age and depressive symptoms on HRQOL were examined. METHOD: Data were obtained from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database. A total of 1034 women underwent coronary angiography between February, 2004 and January, 2005. Questionnaires measuring HRQOL and depressive symptoms were mailed within 1 week of index cardiac catheterization. RESULTS: There were 437 women (42.3%) who responded to the questionnaires. After adjusting HRQOL scores for sociodemographic and clinical variables, depressive symptoms were the strongest predictor of HRQOL; increased age was associated with worse physical functioning and positive disease perception; higher BMI with anginal stability; revascularization with anginal stability and treatment satisfaction. CONCLUSION: Overall, the variables measured accounted for a small proportion of the variance in HRQOL. Further research is needed to understand the complex relationship among age, depressive symptoms, and HRQOL in women with CAD.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.034
GPT teacher head0.292
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.

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

Citations26
Published2008
Admission routes2
Has abstractyes

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