Measurement Properties of Performance-Specific Pain Ratings of Patients Awaiting Total Joint Arthroplasty as a Consequence of Osteoarthritis
Bibliographic record
Abstract
PURPOSE: To estimate the test-retest reliability of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain sub-scale and performance-specific assessments of pain, as well as the association between these measures for patients awaiting primary total hip or knee arthroplasty as a consequence of osteoarthritis. METHODS: A total of 164 patients awaiting unilateral primary hip or knee arthroplasty completed four performance measures (self-paced walk, timed up and go, stair test, six-minute walk) and the WOMAC. Scores for 22 of these patients provided test-retest reliability data. Estimates of test-retest reliability (Type 2,1 intraclass correlation coefficient [ICC] and standard error of measurement [SEM]) and the association between measures were examined. RESULTS: ICC values for individual performance-specific pain ratings were between 0.70 and 0.86; SEM values were between 0.97 and 1.33 pain points. ICC estimates for the four-item performance pain ratings and the WOMAC pain sub-scale were 0.82 and 0.57 respectively. The correlation between the sum of the pain scores for the four performance measures and the WOMAC pain sub-scale was 0.62. CONCLUSION: Reliability estimates for the performance-specific assessments of pain using the numeric pain rating scale were consistent with values reported for patients with a spectrum of musculoskeletal conditions. The reliability estimate for the WOMAC pain sub-scale was lower than typically reported in the literature. The level of association between the WOMAC pain sub-scale and the various performance-specific pain scales suggests that the scores can be used interchangeably when applied to groups but not for individual patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".