A Comparison of Five Multi Attribute Utility Instruments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the results of the validation study carried out to evaluate the Assessment of Quality of Life (AQoL) Instrument for the measurement of health related quality of life and utility. It involves, inter alia, the largest comparison of utility instruments that has been carried out to date. The five instruments included in the study are the AQoL, the Canadian HUI III, the Finnish 15D, the EuroQoL (EQ5D) and the SF36 with UK utility weights as quantified by Brazier (1998). The paper compares: (i) the absolute utility score obtained by different sub-populations; (ii) instrument sensitivity; (iii) the incremental differences in utility between different health states; (iv) the structural properties of descriptive systems; and (v) a limited comparison with a Time Trade-Off (TTO) assessment of own health by individuals. Using these criteria the AQoL performs very well. Its predicted utilities are very similar to those obtained from the HUI. There is evidence that the AQoL has greater sensitivity to health states than other instruments and its psychometric properties, as usually judged, are excellent. Despite this, it is concluded that, at present, no single MAU system can claim to be the gold standard and that researchers should select an instrument that is sensitive to the health states which they are investigating and that caution should be exercised in treating any of the instrument results as representing a utility score which truly represents a trade-off between life and health related quality of life.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it