Systematic review of statistical methods used to analyze Seattle Angina Questionnaire scores.
Bibliographic record
Abstract
BACKGROUND: The Seattle Angina Questionnaire (SAQ) is being used with increasing frequency in clinical research to address the health-related quality of life (HRQOL) outcomes of patients with coronary artery disease. The reliability and validity of the SAQ as a disease-specific HRQOL questionnaire has been established. The purpose of this paper was to systematically identify all studies analyzing SAQ scores, and to review the suitability of the statistical methods used. METHODS: The literature search included all years from the development of the SAQ (1994) to December 2001. Electronic databases were searched using 'Seattle angina questionnaire' as a key word, text word or medical subject heading, as well as combinations of Seattle, angina and questionnaire. The Scientific Citation Index was searched to identify any manuscripts that cited the developmental articles of the SAQ. Relevant manuscripts were identified as studies that used the SAQ as a measurement tool for HRQOL outcome data. RESULTS: Of the 62 studies identified, 14 articles used the SAQ as an outcome measurement tool. The statistical validity of all but one of the 14 studies was doubtful because assumptions required for the use of parametric tests were not addressed and there was no mention of the distributions of the SAQ scores. Based on the designs of the studies, unsuitable analysis methods were used. CONCLUSIONS: Our results demonstrate that investigators may need to increase their attention to the distributional characteristics of their HRQOL data and the design of the study before applying statistical tests to appropriately analyze SAQ HRQOL data.
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 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 | 0.083 | 0.329 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.038 | 0.029 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".