Valuing Health for Clinical and Economic Decisions: Directions Relevant for Rheumatologists
Bibliographic record
Abstract
The quality-adjusted life-year (QALY) is a construct that integrates the value or preference for a health state over the period of time in that health state. The main use of QALY is in cost-utility analysis, to help make resource allocation decisions when faced with choices. Although the concept of the QALY is appealing, there is ongoing debate regarding their usefulness and approaches to deriving QALY. In 2008, OMERACT engaged in an effort to agree on QALY approaches that can be used in rheumatology. Based on a Web questionnaire and a subsequent meeting, rheumatologists questioned whether it was relevant for OMERACT (1) to investigate use of a QALY that represents the patients' perspective, (2) to explore the validity of the visual analog scale (VAS) to value health, and (3) to understand the validity of mapping health-specific instruments on existing preference instruments. This article discusses the pros and cons of these points in light of current insight from the point of view of health economics and decision-making theory. It also considers the further research agenda toward a QALY approach in rheumatology.
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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 | 0.047 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".