Forgotten Joint Score for early outcome assessment after total knee arthroplasty: Is it really useful?
Bibliographic record
Abstract
BACKGROUND: Forgotten Joint Score (FJS) has become a popular tool for total knee arthroplasty (TKA), but almost all studies had assessment performed 1 year after surgery. There is a need for a sensitive tool for earlier outcome assessment. The aim of this study was to investigate the usefulness of FJS within the first year after TKA. METHODS: This was a cross-sectional study. Patients within the first year after primary TKA were recruited. FJS was translated into the local language with a cross-cultural adaptation and was validated by assessing the correlation with the Western Ontario and McMaster Universities Arthritis Index score (WOMAC). Ceiling and floor effects (highest or lowest 10% or 15%) of both scores were compared. Skewness of scores was assessed with a histogram. RESULTS: One hundred sixty-three subjects were recruited: 84 (51.5%) had evaluation at 3 months after the operation, 56 (34.4%) at 6 months, and 23 (14.1%) at 12 months. FJS had fewer patients at the highest 10% (10.7% vs. 16.1%, P = 0.046) or 15% (19.6% vs. 32.1%, P = 0.027) at 6 months and within the first year overall (6.7% vs. 13.5%, P <0.001; 14.1% vs. 22.7%, P <0.001). Also, it had more patients at the lowest 10% (16.7% vs. 0%, P <0.001) or 15% (21.4% vs. 0%, P <0.001) at 3 months, 6 months (10.7% vs. 0%, P <0.001), and overall (12.9% vs. 0%, P <0.001; 16.6% vs. 0%, P <0.001). The skewness was much less than WOMAC (0.09 vs. -0.56). CONCLUSIONS: FJS has a low ceiling effect but a high floor effect in the first year after TKA. Such characteristics make it less useful for the general assessment of early patient report outcome after operation.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".