Developing a crosswalk between the RAND-12 and the health utilities index for multiple sclerosis
Bibliographic record
Abstract
Background: Researchers studying health-related quality of life (HRQOL) in multiple sclerosis (MS) can choose from many instruments, but findings from studies which use different instruments cannot be easily combined. We aimed to develop a crosswalk that associates scores from the RAND-12 to scores on the Health Utilities Index—Mark III (HUI3) in persons with MS. Methods: In 2018, participants in the North American Research Committee on Multiple Sclerosis (NARCOMS) registry completed the RAND-12 and the HUI3 to assess HRQOL. We used item-response theory (IRT) and equipercentile linking approaches to develop a crosswalk between instruments. We compared predicted scores for the HUI3 from each crosswalk to observed scores using Pearson correlations, intraclass correlation coefficients (ICCs), and Bland–Altman plots. Results: Of 11,389 invited participants, 7129 (62.6%) responded. Predicted and observed values of the HUI3 from the IRT-linking method were moderately correlated (Pearson r = 0.76) with good concordance (ICC = 0.72). However, the Bland–Altman plots suggested biased prediction. Predicted and observed values from the equipercentile linking method were also moderately correlated (Pearson r = 0.78, ICC = 0.78). The Bland–Altman plots suggested no bias. Conclusion: We developed a crosswalk between the RAND-12 and the HUI3 in the MS population which will facilitate data harmonization efforts.
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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 | 0.055 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".