EQ‐5D‐5L and SF‐6D health utility index scores in patients with myasthenia gravis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Health utilities are a preference-based method of valuing health states that are used in healthcare research, such as economic evaluations. There are limited health utility valuation data for patients with myasthenia gravis (MG). The aim of the study was to describe health utilities for patients with MG and different health states, using the EQ-5D-5L and SF-6D utility instruments, and to explore clinical and demographic determinants of utilities in this population. METHODS: Patients completed the EQ-5D-5L and SF-6D. In addition, patients were assessed with the Myasthenia Gravis Foundation of America classification, Myasthenia Gravis Impairment Index and MG-QOL15 as disease-specific measures, and the Neuro-QoL Fatigue scale. We calculated mean utilities for each Myasthenia Gravis Foundation of America severity class. We built regression models for the EQ-5D-5L and SF-6D to determine the clinical and demographic factors that determine patients' valuation of their health state. RESULTS: Among 254 patients, mean EQ-5D-5L health utilities were as follows: Remission, 0.94 ± 0.03; Minimal Manifestations, 0.92 ± 0.04; Class I, 0.89 ± 0.06; Class II, 0.78 ± 0.16; Class III, 0.58 ± 0.24 and Class IV, 0.61 ± 0.22. Mean SF-6D health utilities were as follows: Remission, 0.83 ± 0.07; Minimal Manifestations, 0.86 ± 0.14; Class I, 0.82 ± 0.14; Class II, 0.67 ± 0.12; Class III, 0.56 ± 0.11 and Class IV, 0.50 ± 0.10. The limb/axial scores were more highly correlated to health utilities than ocular or bulbar scores. CONCLUSIONS: We present estimates of health utilities for patients with MG that can be used in cost-utility and decision analyses. Limb/axial symptoms had a higher impact on health utilities than ocular or bulbar symptoms, which might reflect the impact of mobility on health valuation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".