Chinese cross-culturally adapted patient-reported outcome measures (PROMs) for knee disorders: a systematic review and assessment using the Evaluating the Measurement of Patient-Reported Outcomes (EMPRO) instrument
Bibliographic record
Abstract
BACKGROUND: Knee patient-reported outcome measures (PROMs) are widely used in research in China, but there is limited evidence on the quality of cross-culturally adapted and original Chinese PROMs. We investigated Chinese language knee PROMs to provide evidence for clinicians on their quality and to guide PROM choices. METHOD: A systematic literature search of databases: PUBMED, CINAHL, EMBASE, and CNKI, using adequate search strings and a three-step screen process identified relevant studies. An independent standardized assessment of the selected studies based on the Evaluating the Measurement of Patient-Reported Outcomes (EMPRO) tool was performed. Inter-rater reliability was assessed using intraclass coefficients (ICC). RESULTS: Thirty-three articles corresponding to 23 knee PROMs were evaluated with EMPRO global scores (100) ranging from 11.11 to 55.42. The attributes 'reliability,' 'validity,' and 'cultural and language adaptation' were significantly better evaluated compared to the attributes 'responsiveness,' 'interpretability,' and 'burden' (for all comparisons p < 0.0001). Moderate-to-excellent inter-rater agreement was observed with ICC values ranging from 0.538 to 0.934. CONCLUSION: We identified six PROMs with a minimum acceptable threshold (> 50/100). The osteoarthritis of knee and hip quality of life, the lower extremity function scale, and the Western Ontario Meniscal Evaluation tool ranked highest. Nevertheless, no single PROM had evidence encompassing all EMPRO attributes, necessitating further studies, especially on responsiveness, interpretability, and burden. We identified duplication of effort as shown by repeated translations of the same PROM; this inefficiency could be ameliorated by rapid approval of Chinese language PROMs documented on original PROM developers' platforms.
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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 | 0.027 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".