Estimation of preference-based measures of health from disease-specific clinical outcome measures for total hip and knee arthroplasty patients
Bibliographic record
Abstract
Transfer to utility (TTU) or mapping methodology allows researchers to estimate a health utility from a disease-specific measure and calculate quality adjusted life years for economic evaluations. The purpose of this study was to develop regression algorithms to map five common disease specific TJA outcome measures to three preference-based health utility scores. An online survey was completed by 438 total hip arthroplasty (THA) patients and 550 total knee arthroplasty patients (TKA). THA patients completed ® the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC ), Harris Hip Score (HHS), and the Hip Disability and Osteoarthritis Outcomes Score (HOOS). ® Knee patients completed the WOMAC , Knee Society Score (KSS), and Knee Disability and Osteoarthritis Outcomes Score (KOOS). All patients completed three preference based questionnaires, the SF-6D, EQ-5D and HUI-3, and responses were used to calculate health utilities. A total of 30 THA mapping models and 30 TKA mapping models were developed and validated. Forecast error measures including ME, MAE, RMSE were defined as our prediction performance criterion. For the THA models, the regression model with HOOS subscores most precisely estimated an EQ-5D health utility. The best performing TKA model mapped the KSS to the EQ-5D. Clinician- researchers can input their disease specific data into these models to estimate health utilities to consider the cost-effectiveness of osteoarthritis-related interventions relative to interventions for very different diseases and conditions.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".