Development and Validation of a Short Version of the Seattle Angina Questionnaire
Bibliographic record
Abstract
BACKGROUND: Clinical trials and national performance measures increasingly mandate reporting patients' perspectives of their health status: their symptoms, function, and quality of life. Although the Seattle Angina Questionnaire (SAQ) is a validated disease-specific health status instrument for coronary artery disease (CAD) with high test-retest reliability, predictive power, and responsiveness, its use in routine clinical practice has been limited, in part, by its length (19 items). METHODS AND RESULTS: Using data from 10 408 patients with CAD from 5 multicenter registries, we derived and validated a shortened version of the SAQ (SAQ-7) among patients presenting with stable CAD, undergoing percutaneous coronary intervention, and after acute myocardial infarction. We examined the psychometric properties of the SAQ-7 as compared with the full SAQ. Seven items from the Physical Limitation, Angina Frequency, and Quality of Life domains were identified for the SAQ-7, with high levels of concordance (0.88-1.00) with each original SAQ domain. The SAQ-7 demonstrated good construct validity (compared with Canadian Cardiovascular Society class for angina), with a correlation of 0.62 and 0.38 for patients with stable CAD and undergoing percutaneous coronary intervention, respectively. It was highly reproducible in patients with stable CAD (intraclass correlation, ≥0.78) and exhibited excellent responsiveness in patients after percutaneous coronary intervention (≥18 points in each SAQ domain). Finally, the SAQ-7 was predictive of 1-year mortality and readmission. CONCLUSIONS: To increase the feasibility of measuring patient-reported outcomes in patients with CAD, we developed and validated a shortened 7-item SAQ instrument for use in clinical trials and routine care.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".