Cross-cultural Adaptation and Validation of the Arabic Version of the Harrus Hip Score
Bibliographic record
Abstract
Abstract Background: The goal of this study was to translate the Harris Hip (HHS) questionnaire into the Arabic language with cross-cultural adaptation to include and benefit Arabic speaking communities as it is the most widely used instrument for disease-specific hip joint evaluation and measurement of total hip arthroplasty outcome. Methods: This questionnaire was translated following A clear and user-friendly guideline protocol. The Cronbach’s alpha was used to assess the reliability and internal consistency of the items of HHS. Additionally, The constructive validity of HHS was evaluated against the 36-Item Short Form Survey (SF 36). Results: A total of 100 participants were included in this study, of which 30 participants were re-evaluated for reliability testing. Cronbach’s alpha of the total score of Arabic HHS is 0.528 and after the standardization, it changed to 0.742 which is within the recommended range (0.7-0.9). Lastly, The correlation between HHS and SF 36 was r=0.71 (p<0.001) which represents a strong correlation between Arabic HHS and SF-36. Conclusion: Based on the results, we believe that the Arabic HHS can be used by clinicians, researchers, and patients to evaluate and report hip pathologies and total hip arthroplasty treatment efficacy.
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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 | 0.010 | 0.020 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".