Measurement Invariance of the Seattle Angina Questionnaire in Coronary Artery Disease
Bibliographic record
Abstract
Abstract PurposeThe Seattle Angina Questionnaire (SAQ) is a widely used patient-reported measure of health status in patients with coronary artery disease. Comparisons of SAQ scores amongst population groups and over time rely on the assumption that its factorial structure is invariant (i.e., equivalent). This study evaluates the measurement invariance of the SAQ across different demographic and clinical groups as well as over time.MethodsData were obtained from the Alberta Provincial Project on Outcome Assessment in Coronary Heart Disease registry, a population-based registry of patients who received coronary angiogram in Alberta, Canada. Health-related quality of life was measured using the 16-item Canadian version of the SAQ (SAQ-CAN). Multi-group confirmatory factor analysis was used to assess configural, weak, strong, and strict measurement invariance (MI) across age groups, sex, disease type, treatment, and over time. Model fit was assessed using the comparative fit index (CFI), and root mean square error of approximation (RMSEA).ResultsOf the 8101 patients who completed the measure at baseline, 1300 (16.1%) were at least 75 years old, while 1755 (21.7%) were female, 5154 (63.6%) were diagnosed with acute coronary syndrome, while 1177(14.5%) received coronary artery bypass graft treatment. There was evidence of strict invariance across age, sex, and disease groups, but partial strict invariance was established across treatment sub-groups and over time.ConclusionSAQ-CAN is a valid measure for comparing health-related quality of life of coronary artery disease patients across population groups and over time.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".