A Scoring Algorithm for Deriving Utility Values from the Neuro-QoL for Patients with Multiple Sclerosis
Bibliographic record
Abstract
Introduction The Neuro-QoL is a standardized approach to assessing health-related quality of life in people with neurological conditions, including multiple sclerosis (MS). Item banks were developed with item response theory (IRT) methodology so items are calibrated along a continuum of each construct. The purpose of this study was to develop a preference-based scoring algorithm for the Neuro-QoL to derive utilities that could be used in economic modeling. Methods With input from neurologists, 6 Neuro-QoL domains were selected based on relevance to MS and used to define health states for a utility elicitation study in the United Kingdom. General population participants and individuals with MS valued the health states and completed questionnaires (including Neuro-QoL short forms). The Neuro-QoL Utility Scoring System (NQU) was derived based on multi-attribute utility theory using data from the general population sample. Single-attribute disutility functions for 6 Neuro-QoL domains were estimated using isotonic regression with linear interpolation and then combined with a multiplicative model. NQU validity was assessed using MS participant data. Results Interviews were completed with 203 general population participants (50.2% female; mean age = 45.0 years) and 62 participants with MS (62.9% female; mean age = 46.1 years). Mean (SD) NQU scores were 0.94 (0.06) and 0.82 (0.13) for the general population and MS samples, respectively. The NQU demonstrated known-groups validity by differentiating among subgroups categorized based on level of disability. The NQU demonstrated convergent validity via correlations with generic measures (0.66 and 0.63 with EQ-5D-5L and Health Utilities Index Mark 3, respectively; both P < 0.001). Discussion With the NQU, utilities can be derived from any MS treatment group, subgroup, or patient sample who completes items from 6 Neuro-QoL domains. Because the Neuro-QoL is frequently used with MS patients, the NQU greatly expands the options for quantifying outcomes in cost-utility analyses conducted to inform allocation of resources for MS treatment.
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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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".