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Trajectories of Health-Related Quality of Life in Coronary Artery Disease

2018· article· en· W2789656655 on OpenAlexafffundabout
Tolulope T. Sajobi, Meng Wang, Olu Awosoga, Maria Santana, Danielle A. Southern, Zhiying Liang, Diane Galbraith, Stephen B. Wilton, Hude Quan, Michelle M. Graham, Matthew T. James, William A. Ghali, M.L. Knudtson, Colleen M. Norris

Bibliographic record

VenueCirculation Cardiovascular Quality and Outcomes · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversity of AlbertaUniversity of Lethbridge
FundersCanadian Institutes of Health Research
KeywordsMedicineCoronary artery diseaseAnginaPsychological interventionQuality of life (healthcare)Myocardial infarctionPhysical therapyInternal medicineDiabetes mellitusBody mass indexLogistic regressionPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Health-related quality of life (HRQOL) assessment is an important health outcome for measuring the efficacy of treatments and interventions for coronary artery disease (CAD). HRQOL is known to improve over the first year after interventions for CAD, but there is limited knowledge of the changes in HRQOL beyond 1 year. We investigated heterogeneity in long-term trajectories of HRQOL in patients with CAD. METHODS AND RESULTS: Data were obtained from 6226 patients identified from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease with at least 1-vessel CAD who underwent their first catheterization between 2006 and 2009. HRQOL was assessed using the Seattle Angina Questionnaire, a 19-item disease-specific measure of HRQOL for patients with CAD. Group-based trajectory analysis was used to identify various subgroups of Seattle Angina Questionnaire trajectories over time while adjusting for missing data through a longitudinal multiple imputation model. Multinomial logistic regression was used to identify the predictors of differences among the identified subgroups. Our analysis revealed significant improvements in HRQOL across all the 5 domains of Seattle Angina Questionnaire overtime for the whole data. Multitrajectory analyses revealed 4 HRQOL trajectory subgroups including high (25.1%), largely increased (32.3%), largely decreased (25.0%), and low (17.6%) trajectories. Age, sex, body mass index, diabetes mellitus, previous history of myocardial infarction, smoking, depression, anxiety, type of treatment received, and perceived social support were significant predictors of differences among these trajectory subgroups. CONCLUSIONS: This study highlights variations in longitudinal trajectories of HRQOL in patients with CAD. Despite overall improvements in HRQOL, about a quarter of our cohort experienced a significant decline in their HRQOL over the 5-year period. Understanding these HRQOL trajectories may help personalize prognostic information, identify patients and HRQOL domains on which clinical interventions are most beneficial, and support treatment decisions for patients 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 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)0.004
metaresearch head score (Gemma)0.011
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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.091
GPT teacher head0.372
Teacher spread0.281 · 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

Citations71
Published2018
Admission routes3
Has abstractyes

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