Comparison of the standard gamble, rating scale, AQLQ and SF-36 for measuring quality of life in asthma
Bibliographic record
Abstract
With interest in health economics growing, there is a demand for valid methods for measuring health-related quality of life (HRQL) in asthma using utilities. The aims of this study were to develop disease-specific versions of the standard gamble and rating scale, to compare their measurement properties with those of the Asthma Quality of Life Questionnaire (AQLQ) and the Medical Outcomes Survey Short-Form 36 (SF-36), as well as to determine their validity for assessing asthma-specific quality of life. Forty adults with symptomatic asthma participated in a 9-week observational study. Participants completed the standard gamble, rating scale, AQLQ, SF-36 and other measures of clinical asthma status at baseline and after 1, 5 and 9 weeks. In patients whose asthma was stable between assessments, reliability was good for the rating scale (intraclass correlation coefficient (ICC)=0.89) and the AQLQ (ICC=0.95) but more modest for the SF-36 mental score (ICC=0.68), SF-36 physical score (ICC=0.65) and standard gamble (ICC=0.59). The responsiveness index was highest in the AQLQ (1.35), followed by the rating scale (0.74), the physical score of the SF-36 (0.61) and the standard gamble (0.31). Construct validity (correlation with other indices of health status) was strongest for the AQLQ and the rating scale. In conclusion, both the disease-specific rating scale and the Asthma Quality of Life Questionnaire have strong measurement properties for measuring asthma-specific quality of life; the Short-Form 36 health survey physical summary score has more modest properties. Although the disease-specific standard gamble has acceptable discriminative properties, its evaluative properties are too inadequate for it to be used in cost/utility analyses. Poor correlation between the standard gamble and the rating scale indicates that utilities cannot be derived from rating scale data.
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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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 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".