1225: Health-related Quality of Life Differences Between Women and Men Treated for Coronary Artery Disease in Alberta, Canada
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".