Cross-Cultural Adaptation and Validation of the Persian Version of the Harris Hip Score
Bibliographic record
Abstract
Background The Persian language, also known as Farsi, is a pluricentric language spoken in Iran, Afghanistan, and Tajikistan by about 140 million people. This study aims to translate the Harris hip score (HHS) into Persian with cross-cultural adaptation and to evaluate its validity and reliability. Methods One hundred fifty-six total hip arthroplasty patients completed the Persian version of the HHS, Western Ontario and McMaster Universities Osteoarthritis Index, Forgotten Joint Score, and visual analog scale (VAS) for pain and satisfaction postoperatively. Using Cronbach's alpha (α) coefficient, internal consistency was evaluated. Correlations (Spearman's Rho) were used to assess validity. A test-retest reliability assessment of the Persian HHS was conducted (n = 47) using the intraclass correlation coefficient. Content validity was evaluated using the floor and ceiling effects of the HHS. Results The final translation of the Persian HHS was approved to be used. The preoperative and postoperative Cronbach's alpha were 0.71 and 0.70, respectively, and showed acceptable internal consistency. The intraclass correlation coefficient was excellent (0.869, P < .001). Insignificant ceiling effects (13.5%) and no floor effects (0) were observed. The HHS score was significantly and strongly correlated with Western Ontario and McMaster Universities Osteoarthritis Index (r = 0.696, P < .001), VAS pain (r = 0.654, P < .001), VAS satisfaction (r = 0.634, P < .001), and Forgotten Joint Score (r = 0.648, P < .001). Conclusions The Persian HHS demonstrated excellent reliability and validity properties. Accordingly, Persian HHS may be a helpful tool for assessing patients undergoing total hip arthroplasty.
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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 | 0.011 | 0.016 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".