A comparison of the Assessment of Quality of Life (AQoL) with four other generic utility instruments
Bibliographic record
Abstract
As part of the validation of the Assessment of Quality of Life (AQoL) instrument comparisons were made between five multiattribute utility (MAU) instruments, each purporting to measure health-related quality of life (HRQoL). These were the AQoL, the Canadian Health Utilities Index (HUI) 3, the Finnish 15D, the EQ-5D (formerly the EuroQoL) and the SF6D (derived from the SF-36). The paper compares absolute utility scores, instrument sensitivity, and incremental differences in measured utility between different instruments predicted by different individuals. The AQoL predicted utilities are similar to those from the HUI3 and EQ-5D. By contrast the 15D and SF6D predict systematically higher utilities, and the differences between individuals are significantly smaller. There is some evidence that the AQoL has greater sensitivity to health states than other instruments. It is concluded that at present no single MAU instrument can claim to be the 'gold standard', and that researchers should select an instrument sensitive to the health states they are investigating. Caution should be exercised in treating any of the instrument scores as representing a trade-off between length of life and HRQoL.
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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 | 0.021 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".