Crosscultural adaptation reliability and validity of the Turkish version of the Hospital for Special Surgery HSS Knee Score
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to adapt the English version of the Hospital for Special Surgery (HSS) knee score for use in a Turkish population and to evaluate its validity, reliability and cultural adaptation. METHODS: Standard forward-back translation of the HSS knee score was performed and the Turkish version was applied in 73 patients. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Mini-Mental State Examination and sit-to-stand test were also performed and analyzed. Internal consistency reliability was tested using Cronbach's alpha. The intraclass correlation coefficient (ICC) was used to calculate the test-retest reliability at one-week intervals. Validity was assessed by calculating the Pearson correlation between the HSS, WOMAC and sit-to-stand test scores. RESULTS: The ICC ranged from 0.98 to 0.99 with high internal consistency (Cronbach's alpha: 0.87). The WOMAC score correlated with total HSS score (r: -0.80, p<0.001) and sit-to-stand score (r: 0.12, p: 0.312). CONCLUSION: The Turkish version of the HSS knee score is reliable and valid in evaluating the total knee arthroplasty in Turkish patients.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".