MétaCan
Menu
Back to cohort
Record W2072480338 · doi:10.1177/147451510300200125

1225: Health-related Quality of Life Differences Between Women and Men Treated for Coronary Artery Disease in Alberta, Canada

2003· article· en· W2072480338 on OpenAlexaffabout
Colleen M. Norris, William A. Ghali, Leah Jensen, Diane Galbraith, Merril L. Knudtson

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineCoronary artery diseaseDiseaseGerontologyQuality of life (healthcare)Internal medicineCardiologyNursing

Abstract

fetched live from OpenAlex

Introduction: Although there have been substantial medical advances that improve the survival rates of cardiac ischemic events, gender differences in recovery for patients with coronary artery disease (CAD) continue to exist. There is a paucity of data comparing health-related quality of life (HRQOL) in men and women undergoing treatment for CAD. The purpose of this study was to compare the HRQOL outcomes of men and women in Alberta, Canada treated with coronary artery bypass graft surgery (CABG), percutaneous coronary intervention (PCI with/without stent), or medical therapy, at or near 1-year following initial cardiac catheterization, after adjustment for known demographic, co-morbid, and disease severity predictors of outcomes. Method: The HRQOL outcome data were collected by means of a self-reported questionnaire mailed to patients on or near the 1-year anniversary of their initial cardiac catheterization and entered into the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database. The self-reported questionnaire was the Seattle Angina Questionnaire (SAQ) that consists of 19 items. Five dimensions of CAD are measured: exertional capacity, anginal stability, anginal frequency, quality of life and treatment satisfaction generating five independent scales. Two strategies were used to risk adjust the SAQ scale scores. The first strategy was to use the proportional odds (PO) model sometimes referred to as the ‘ordinal logistic’ modeling. The second strategy was to use general least squares linear modeling (GLM).

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)0.026
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.028
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.172
GPT teacher head0.333
Teacher spread0.161 · 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

Citations1
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueEuropean Journal of Cardiovascular NursingSame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207