Ultracongruent Versus Posterior-Stabilized Polyethylene: No Difference in Anterior Knee Pain but Decreased Noise Generation
Bibliographic record
Abstract
BACKGROUND: Noise generation and anterior knee pain can occur after primary total knee arthroplasty (TKA) and may affect patient satisfaction. Polyethylene design in cruciate-sacrificing implants could be a variable influencing these complications. The purpose of this study was to analyze the effect of polyethylene design on noise generation and anterior knee pain. METHODS: We prospectively reviewed a cohort of patients who underwent primary TKA between 2014 and 2022 by a single surgeon using either a posterior-stabilized (PS) or ultracongruent (UC) polyethylene of the same implant design. The primary outcomes were measured through a noise generation questionnaire and the Knee Injury and Osteoarthritis Outcome Score-Patellofemoral score. RESULTS: A total of 409 TKA procedures were included, 153 (37.4%) PS and 256 (62.6%) UC. No difference was noted in the Knee Injury and Osteoarthritis Outcome Score-Patellofemoral score between PS and UC designs (71.7 ± 26 versus 74.2 ± 23.2, P = 0.313). A higher percentage of patients in the PS cohort reported hearing (32.7% versus 22.3%, P = 0.020) or feeling noise (28.8 versus 20.3, P = 0.051) coming from their implant. No notable difference was observed in noise-related satisfaction rates. Independent risk factors of noise generation were age (OR, 0.96; P = 0.006) and PS polyethylene (OR, 1.61; P = 0.043). Noise generation was associated with decreased patient-reported outcome measure scores ( P < 0.001). CONCLUSION: While there was no difference in anterior knee pain between PS and UC polyethylene designs, PS inserts exhibit higher rates of noise generation compared with UC. Noise generation had comparable satisfaction but was associated with decreased patient-reported outcome measure scores.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".