Psychometric properties of the Italian version of the Forgotten Joint Score in patients with total hip arthroplasty
Bibliographic record
Abstract
Total hip arthroplasty (THA) surgeries are increasing; to assess quality of life after THA, an instrument that considers patient's perspective on surgical outcomes is necessary. The objective of this study is to assess the psychometric properties of the Italian version of the Forgotten Joint Score (FJS-I) in patients with THA. The FJS-I was administered to 111 patients with THA, as well as the Western Ontario and McMaster Universities (WOMAC), Numerical Pain Rating Scale (NPRS), and the EuroQol 5D-5L (EQ-5D-5L). Structural validity [confirmatory factor analysis (CFA)], internal consistency (Cronbach's alpha), test-retest reliability [intraclass correlation coefficient (ICC 2,1 )], measurement error [standard error of the measurement (SEM)], and construct validity (hypothesis testing with correlation of the WOMAC, NPRS, and EQ-5D-5L) were assessed. In addition, the minimal detectable change (MDC) was computed. The result of CFA confirmed the one-factor structure. Internal consistency was supported (α = 0.944). A high test-retest reliability (ICC = 0.958; 95% confidence interval, 0.914-0.980) was found with an SEM and an MDC of 5.3 and 16.6 points, respectively. The a-priori hypotheses were fully met, determining the construct validity to be satisfactory. Psychometric properties of the FJS-I were confirmed, and it can be used for single-person assessment. Further research is suggested to refine its structural validity.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".