Translation and Validation of the Korean Version of the Forgotten Joint Score
Bibliographic record
Abstract
BACKGROUD: The Forgotten Joint Score (FJS) is a newly developed patient-reported outcome measure designed to evaluate clinical outcome after total knee arthroplasty (TKA). The FJS is known as a sensitive test with a low ceiling effect. It has been recently translated into many languages. However, no study has reported the validity or reliability of a Korean version of the FJS (K-FJS). Thus, the purpose of this study was to address this issue. METHODS: According to guidelines for cross-cultural adaptation, translation of the English version of the FJS was performed. After obtaining a license from the original developer, 150 patients who had undergone TKA at more than 1 year to less than 5 years ago completed the K-FJS, visual analog scale, Western Ontario and McMaster Universities Osteoarthritis index (WOMAC), and the 36-Item Short Form (SF-36) health survey. To measure test-retest reliability, the K-FJS was completed twice by telephone survey for 100 patients. Responsiveness was retrospectively calculated based on a survey of 50 patients at 3 months and 1 year after surgery. RESULTS: = 0.143). At 3 to 12 months after TKA, the standardized response mean (SRM) was 0.67, which was lower than the SRM of WOMAC (1.03) obtained in the same period. The K-FJS demonstrated strong measurement properties in terms of good construct validity and reliability. CONCLUSIONS: This study suggests that the K-FJS is an excellent instrument that can be used to monitor clinical outcomes after TKA. Using this standardized K-FJS, it would be possible for medical institutions to share more accurate clinical results.
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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 | 0.016 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".