Reliability and validity of patient-reported outcome measures in assessing knee osteoarthritis in the Chinese population: A systematic review
Bibliographic record
Abstract
Objective: Knee osteoarthritis (KOA) is a prevalent condition in China, necessitating effective assessment tools for treatment outcomes. This study systematically reviews and analyzes the reliability, validity, and selection of patient-reported outcome measures (PROMs) for evaluating KOA. Methods: Following PRISMA guidelines, a literature search was conducted across seven databases, including CNKI, PubMed, and Embase, covering publications from December 2012 to December 2022. The methodological quality of the studies was assessed using the COSMIN checklist. Results: Twenty-one studies met the inclusion criteria, involving eight types of KOA PROMs. The Oxford Knee Score (OKS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were the most frequently utilized, appearing in nine and four studies, respectively. OKS achieved a "strong" rating in internal consistency, test-retest reliability, content validity, responsiveness, and measurement error, while WOMAC received a "strong" rating in internal consistency, test-retest reliability, and content validity, with a "moderate" rating in structural validity. Conclusion: Both OKS and WOMAC are effective PROMs for evaluating KOA in China. However, the choice of a specific tool should be based on the study's objectives and the practical context, considering each tool's reliability, validity, and other measurement properties.
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 | 0.024 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".