Optimizing outcomes in total knee arthroplasty: the role of patellar resurfacing and tibial ınsert type
Bibliographic record
Abstract
OBJECTIVE: Total knee arthroplasty is a common procedure for advanced knee osteoarthritis, aiming to reduce pain and restore function. However, the impact of patellar resurfacing and tibial insert types (fixed vs. mobile) on clinical outcomes remains debated, with limited comparative studies. METHODS: This retrospective cohort study included total knee arthroplasty patients (2012-2018) with ≥5 years of follow-up, divided into four groups based on insert type and resurfacing status. Clinical outcomes were assessed using visual analog scale, Western Ontario and McMaster Universities Osteoarthritis Index, timed up and go, and range of motion. Shapiro-Wilk tests were used to assess normality, and group comparisons were conducted using non-parametric statistical methods. RESULTS: Non-resurfacing groups had significantly higher pain scores (p<0.001). Mobile inserts provided better flexion range of motion and Western Ontario and McMaster Universities Osteoarthritis Index functional scores than fixed inserts (p<0.001). The best functional outcomes were observed in the mobile insert with the resurfacing group. A significant correlation was found between timed up and go and Western Ontario and McMaster Universities Osteoarthritis Index total scores in the fixed insert without resurfacing group (r=0.424, p=0.008), while no such correlation was observed in other groups. CONCLUSION: Patellar resurfacing and mobile tibial inserts enhance pain relief, mobility, and function in total knee arthroplasty patients. However, due to the retrospective nature of the study and group heterogeneity, prospective multicenter trials are warranted to validate these findings. These findings emphasize the importance of individualized implant selection, warranting further prospective, multicenter studies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".