Psychometric properties of the Arabic version of the Forgotten Joint Score usage in total hip arthroplasty
Bibliographic record
Abstract
BACKGROUND: The ultimate goal of arthroplasty is thought to be the ability to "forget" a joint implant in daily activities. The Forgotten Joint Score (FJS-12), a score system that evaluates how much patients have been able to forget their hip or knee prosthesis, was recently published. It is based on a self-administered questionnaire that consists of 12 items. The major goal of the current study was to validate, adapt, and evaluate a Arabic-language FJS-12 (Ar-FJS-12) version in patients who had undergone total hip replacement (THA). MATERIALS AND METHODS: The study included 107 patients who underwent THA 1-5 years ago and completed the Ar-FJS. The construct validity of the study was evaluated using the reduced Western Ontario and McMaster Universities Osteoarthritis Index (rWOMAC). To assess the test-retest reliability, 72 people took the Ar-FJS test twice. RESULTS: Cronbach's alpha (Internal Consistency) of the Ar-FJS-12 was 0.957 and the intraclass correlation coefficient (ICC) was 0.931 indicating high reliability. For construct validity, there was a moderate significant correlation between the Arabic the rWOMAC with r = 0.595. The ceiling effect was 1.9% (n = 2), whereas the floor effect was 1.9% (n = 2). CONCLUSION: The Arabic version of the FJS-12 valid, reliable tool and can be recommended for patients in Arabic-speaking communities who have undergone hip arthroplasty. LEVEL OF EVIDENCE: III, validity and reliability study.
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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 | 0.006 | 0.029 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".