Health State Utility Values Associated with Knee Osteoarthritis: A Vignette-Based Approach
Bibliographic record
Abstract
Juan M Ramos-Goñi,1,2 Mathieu F Janssen,1 Magaly Perez-Nieves,3 Oliver Rivero-Arias,1,4 Sylvia Gonsahn-Bollie,3 Kristina S Boye3 1Maths in Health, Klimmen, Limburg, the Netherlands; 2Decision Analysis and Support Unit, SGH. Warsaw School of Economics, Warsaw, Poland; 3Eli Lilly and Company, Indianapolis, IN, USA; 4Nuffield Department of Population Health, National Perinatal Epidemiology Unit, University of Oxford, Oxford, UKCorrespondence: Juan M Ramos-Goñi, Maths in Health, Klimmen, Limburg, the Netherlands, Tel +34670757100, Email jramos@mathsinhealth.comPurpose: Pain is the most common symptom of Osteoarthritis (OA) making OA one of the most frequent causes of mobility dependence and disability and resulting in a significant negative impact on health-related quality of life (HRQoL). The main objective of this study was to estimate health state utility values (HSUVs) associated with different levels of pain related to Knee OA (KOA).Patients and Methods: Six different health state vignettes were developed using best practices and real-world data from the Osteoarthritis Initiative (OAI) database that included the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) instrument. OAI data from individuals with KOA were categorized into 6 different pain profiles using patient responses to the WOMAC pain items (pain while: walking; climbing stairs; sleeping; resting and standing) each having response levels 0 (no)-4 (extreme). The six vignettes identified the most frequently observed response levels of the pain items. A time trade-off study was conducted in the UK among individuals with KOA.Results: Analysis dataset included 198 interviews. Participants’ mean age was 51.6 years and 58.6% were females. Mean HSUVs ranged from 0.983 for the mildest health state, which was described as slight pain while climbing stairs and no pain on the other items, to 0.305 for the most severe health state which was described as extreme pain in all items.Conclusion: This is the first known set of HSUVs estimated describing levels of pain most commonly reported by individuals with KOA. The results demonstrate considerable HRQoL burden in individuals with KOA.Keywords: vignettes, time-trade-off, knee osteoarthritis, overweight/obesity, pain
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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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".