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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".