Validation and interval scale transformation of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) in patients undergoing knee arthroplasty, using the Rasch model
Bibliographic record
Abstract
Objectives: WOMAC) to the Rasch model and derive the transformation table for interval scale measurement. Methods: Data from osteoarthritis patients listed for knee arthroplasty (KA) pre-operatively, and at 6- and 12- months post-operative was used. WOMAC was calibrated for fit to the Rasch model for monotonicity, homogeneity, local item independence and absence of differential item functioning (DIF) in a randomly selected 900 patients, 300 from each time point; parameter estimates were then imported into the full data set. Responsiveness was reported through Standard Error of Measurement (SEM); Smallest Detectable Difference (SDD), %SDD and effect sizes (ES) between baseline and 6-months. WOMAC was transformed from ordinal to interval values. Results: 1136 patients (mean age 65.9 years, 69.9% female) were included. WOMAC pain (0-20), function (0-68) and total scores (0-96) had adequate fit to Rasch model with good reliability (Person Separation Index: 0.76, 0.80 and 0.79). No item deletion was required. The SEM, SDD, %SDD and ES of WOMAC total were 4.4, 6.9, 10.1, and 1.97. No significant DIF was seen for age, sex, body mass index, type of KA, languages, and education level. WOMAC pain, function and total scores were transformed to interval scales. Conclusion: WOMAC total, pain and function scales had adequate fit to the Rasch model, providing unidimensional measure with good reliability and responsiveness. Transformation of WOMAC to interval scale measurement is applicable to other studies.
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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 | 0.029 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".