Correction: Scale Characteristics and Mapping Accuracy of the US EQ-5D, UK EQ-5D, and SF-6D in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
Objective.To compare the US EQ-5D with the UK EQ-5D and the SF-6D in patients with rheumatoid arthritis (RA).To provide mappings for each of the scales based on clinical variables.Methods.We studied 12,424 patients with RA with 66,958 longitudinal observations using linear regression.In our mapping models we used the Health Assessment Questionnaire (HAQ) as a continuous predictor variable and as individual items.More complex models included the addition of a visual analog pain scale, the mood scale from the SF-36, and demographic and comorbidity covariates.We compared various models using root mean squared error (RMSE), in-sample and out-of-sample mean absolute error (MAE), and other measures of prediction accuracy and model fit.Results.At any level of clinical severity, the US EQ-5D always had a higher utility score than the UK EQ-5D; and overall, the US scores were 0.094 units higher.The best models explained 64% to 72% of variance in utility scores, with RMSE values of 0.07 (SF-6D), 0.11 (EQ-5D US), and 0.17 (UK EQ-5D).There was a substantial increase in predictive accuracy by using pain and mood as predictor variables in the mapping. Conclusion.The US EQ-5D differs from the UK version and from the SF-6D in mean scores and ranges.When determined by mapping, the US EQ-5D has a much lower prediction error than the UK EQ-5D.Simple mapping models that use HAQ and pain have acceptable error rates, although more complex models that include mood scores and individual HAQ items substantially improve predictive accuracy.
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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 | 0.033 | 0.390 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 0.011 |
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".