Joint awareness after unicompartmental knee arthroplasty and total knee arthroplasty: a systematic review and meta‐analysis of cohort studies
Bibliographic record
Abstract
Abstract Purpose The purpose of this systematic review and meta‐analysis is to evaluate the joint awareness after unicompartmental knee arthroplasty (UKA) and total knee arthroplasty (TKA). It was hypothesized that patients with UKA could better forget about their artificial joint in comparison to TKA. Methods A search of major literature databases and bibliographic details revealed 105 studies evaluating forgotten joint score in UKA and TKA. Seven studies found eligible for this review were assessed for risk of bias and quality of evidence using the Newcastle–Ottawa Scale. The forgotten joint score (FJS‐12) was assessed at 6 months, 1 year, and 2 years. Results The mean FJS‐12 at 2 years was 82.35 in the UKA group and 74.05 in the TKA group. Forest plot analysis of five studies (n = 930 patients) revealed a mean difference of 7.65 (95% CI: 3.72, 11.57, p = 0.0001; I2 = 89% with p < 0.0001) in FJS‐12 at 2 years. Further sensitivity analysis lowered I2 heterogeneity to 31% after exclusion of the study by Blevin et al. (MD 5.88, 95%CI: 3.10, 8.66, p < 0.0001). A similar trend of differences in FJS‐12 between the groups was observed at 6 months (MD 32.49, 95% CI: 17.55, 47.43, p < 0.0001) and at 1 year (MD 25.62, 95% CI: 4.26, 46.98, p = 0.02). Conclusions UKA patients can better forget about their artificial joint compared to TKA patients. Level of evidence III.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".