Predicting EQ-5D Utility Scores from the Seattle Angina Questionnaire in Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: The Seattle Angina Questionnaire (SAQ), a descriptive quality of life instrument, is often used in coronary artery disease studies. In its current form, however, it cannot be used in economic evaluations. The investigators sought to create a mapping algorithm that would allow translation of SAQ scores into EQ-5D utility scores. METHODS: Data from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database were used to examine the relationship between scores in each of the 5 domains of the SAQ (physical limitation, anginal stability, anginal frequency, treatment satisfaction, and disease perception) and the EQ-5D utility score. The cohort was divided into 80% derivation and 20% validation sets. Mapping algorithms were developed using simple linear regression and Tobit models. To account for the skewed distribution of the EQ-5D scores and the presence of heteroscedasticity, Bayesian extensions were applied to each model by specifying a nonconstant variance for the error term. Model performance was assessed by comparing predicted and observed mean EQ-5D scores in the validation set, and the unadjusted R2. RESULTS: The cohort consisted of 1992 patients. The simple linear regression model had the best predictive performance, with an R2 of 0.38. The nonconstant variance term did not improve overall performance for any of the models. The linear regression model accurately estimated the mean EQ-5D score in the validation set (predicted score 0.81 v. observed score 0.81). CONCLUSIONS: Mean EQ-5D utility weights can be accurately estimated from the SAQ using a simple linear regression mapping algorithm.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".