Responsiveness of the Persian Version of Forgotten Joint Score-12 Questionnaire After Lower Limb Surgery
Bibliographic record
Abstract
Background and Objectives: The Persian version of the forgotten joint score-12 (FJS-12) questionnaire with acceptable reliability and validity is used to measure the extent of awareness of the joint and the ability of the patient in forgetting the involved joint. This study aims to evaluate the responsiveness of the Persian version of FJS-12 among Persian-speaking subjects following anterior cruciate ligament (ACL) surgery, total knee arthroplasty (TKA), and total hip arthroplasty (THA). Methods: Twenty-five candidates for ACL surgery, 54 subjects with TKA, and 33 subjects with THA participated in this study. They filled the FJS-12 and Western Ontario and McMaster Universities osteoarthritis index (WOMAC) questionnaires before the surgery and 7 weeks after surgery as well as after physiotherapy. For analysis, receiving operator characteristic (ROC) method and gamma correlation coefficient were used. Results: The receiving ROC area under the curve (AUC) for FJS-12 and WOMAC index was 0.77 for both, this meant acceptable value, and minimal clinically important change (MCIC) was 32.45 for FJS-12. Finally, the gamma correlation coefficient of the questionnaire was estimated at an average of 0.5. Conclusion: The FJS-12 questionnaire has acceptable responsiveness that can be used to evaluate the therapeutic effects in clinical settings.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".