Development of one general and six country-specific algorithms to assess societal health utilities based on ASAS HI
Bibliographic record
Abstract
Objective: Health utilities represent preference values that persons attach to health states. This study aims to develop one general and six country-specific algorithms to calculate societal preference values for health of patients with spondyloarthritis (SpA), as assessed by the disease-specific Assessment of SpondyloArthritis international Society Health Index (ASAS HI). Methods: A survey was performed in random population samples from six European countries. In a best-worst choice experiment, subjects were asked to indicate repeatedly which of 4 random aspects of the 17-item ASAS HI was were most and least important. Bayesian analysis provided the relative importance of each of the 17 items. To rescale the relative importance scores on the absolute utility scale between 0 and 1, participants additionally completed two lead time trade-off experiments, one for 'severe SpA' and one for 'best health' without SpA. Six country-specific algorithms and one general algorithm were derived. The general algorithm was tested in 199 patients with axial SpA (axSpA). Results: 3039 subjects, mean age 47 years (SD 15) and 52% female completed the experiments. The population's health utility value for SpA varied between - 0.24 for 'worst' SpA (country range -0.35 to 0.03), and 0.88 for 'best' health (country range 0.81 to 0.90). Among 199 patients with axSpA, the mean utility was 0.36 (SD 0.30, range -0.24 to 0.88) and discriminated well between patients having high (Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) ≥ 4) or low (BASDAI < 4) disease activity (0.18 (SD 0.24) vs 0.51(SD 0.27), p<0.01). Conclusion: One general and six country-specific algorithms are available to convert scores from the ASAS HI into disease-specific societal utility values.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".