Translation, Cross-Cultural Adaptation and Validation of the Chinese Version of the High Activity Arthroplasty Score
Bibliographic record
Abstract
Background: The High Activity Arthroplasty Score (HAAS) is a validated score that assesses functional outcomes after lower limb arthroplasty, with fewer ceiling effects than other scores. The aim is to translate and cross-culturally adapt the HAAS into a Chinese version (HAAS-C) and to evaluate the psychometric properties of HAAS-C in patients after primary total knee arthroplasty (TKA). Methods: A total of 104 patients diagnosed with knee osteoarthritis who had undergone TKA at least 12 months prior were recruited. A forward and backward translation procedure was performed for developing a culturally acceptable HAAS-C. Internal consistency was assessed using Cronbach’s α, and test-retest reliability was measured using the intraclass correlation coefficient (ICC) within a 10-day interval. Construct validity was assessed by examining the correlations between HAAS-C and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), EuroQoL Group’s five-dimension questionnaire (EQ-5D-5L), and Oxford knee score (OKS). Results: HAAS-C demonstrated adequate Internal consistency reliability, as indicated by Cronbach’s α coefficient of 0.75. Test-retest reliability yielded excellent results, with an ICC value of 0.98. Content validity indices were high, with a scale-level validity index of 0.9 and item-level validity indices greater than or equal to 0.8. HAAS-C showed a strong correlation with WOMAC ( r = 0.69), a moderate correlation with EQ-5D-5L ( r = 0.43), and OKS ( r = 0.53) while exhibiting no floor or ceiling effects. Conclusion: The validated HAAS-C questionnaire is a valid instrument for assessing patients undergoing TKA in mainland China. Keywords: high activity arthroplasty score, total knee arthroplasty, Chinese validation, reliability, validity
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".