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 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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".