Problems persist in reporting of methods and results for the WOMAC measure in hip and knee osteoarthritis trials
Bibliographic record
Abstract
PURPOSE: The Western Ontario and McMaster Universities Arthritis Index (WOMAC) is a commonly used outcome measure for osteoarthritis. There are different versions of the WOMAC (Likert, visual analogue or numeric scales). A previous review of trials published before 2010 found poor reporting and inconsistency in how the WOMAC was used. This review explores whether these problems persist. METHODS: This systematic review included randomised trials of hip and/or knee osteoarthritis published in 2016 that used the WOMAC. Data were extracted on the version used, score range, analysis and results of the WOMAC, and whether these details were clearly reported. RESULTS: This review included 62 trials and 41 reported the WOMAC total score. The version used and item range for the WOMAC total score were unclear in 44% (n = 18/41) and 24% (n = 10/41) of trials, respectively. The smallest total score range was 0-10 (calculated by averaging 24 items scored 0-10); the largest was 0-2400 (calculated by summing 24 items scored 0-100). All trials reported the statistical analysis methods but only 29% reported the between-group mean difference and 95% confidence interval. CONCLUSION: Details on the use and scoring of the WOMAC were often not reported. We recommend that trials report the version of the WOMAC and the score range used. The between-group treatment effect and corresponding confidence interval should be reported. If all the items of the WOMAC are collected, the total score and individual subscale scores should be presented. Better reporting would facilitate the interpretation, comparison and synthesis of the WOMAC score in trials.
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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 | 0.815 | 0.948 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.020 |
| Bibliometrics | 0.021 | 0.028 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".