Design Modifications of the Posterior-Stabilized Knee System May Reduce Anterior Knee Pain and Complications following Total Knee Replacement
Bibliographic record
Abstract
BACKGROUND: In total knee arthroplasty (TKA), advances in posterior-stabilized (PS) knee implant designs address patellofemoral mechanics and cam-post engagement in an effort to reduce patellofemoral pain and improve knee kinematics. Such modifications may include improved femoral rollback, improved femoral dislocation resistance, minimized wear, and improved longevity. QUESTIONS/PURPOSES: In this study, we compared a newer PS knee design that incorporates a left and right specific femoral component and smoother trochlear groove to improve patellofemoral mechanics with an older PS design in order to assess patellofemoral pain, manipulation rates, and revision rates. METHODS: PS knee system and the older Optetrak PS knee system (Exactech Inc., Gainesville, FL, USA), with a minimum 2-year follow-up. Clinical outcomes for each cohort were measured using the Knee Society Clinical Rating System, University of California Los Angeles Activity Scale, pain visual analog scale (VAS), Veterans Rand 12-Item Health Survey, and Western Ontario and McMaster Universities Osteoarthritis Index. In addition, rates of anterior knee pain, manipulation, and revision were compared between the two knee systems, and a Kaplan-Meier survivorship curve defining failure as need for revision was calculated to allow comparison between the cohorts. RESULTS: From 2000 to 2018, there were 1482 TKAs performed using the Logic PS (not counting 12 patients who had died) and 445 in the Optetrak PS group (not counting 20 patients who had died). In the Logic PS and Optetrak PS groups, respectively, the average age at operation was 66.7 years and 68.6 years and the average body mass index was 30.8 and 31.2. Pain VAS scores were significantly lower in the Logic PS group than in the Optetrak group (1.72 vs. 2.75 out of 10, respectively). There was also a significant difference in the percentages of patients reporting anterior knee pain in the Logic group, as compared with the Optetrak group (5.6% vs. 11.8%, respectively). In addition, manipulation rates differed significantly between the Logic and Optetrak groups (0.34% vs. 10.70%, respectively). The revision rates were 1.15% for the Logic group and 2.0% for the Optetrak group. However, there was a significant difference in rates of revision performed because of osteolysis, favoring the Logic group (0.07% vs. 0.6%). The Kaplan-Meier survivorship curve shows a significant difference in time until revision between the Logic and Optetrak groups. CONCLUSION: Design modifications to improve patellofemoral mechanics demonstrated significant improvements in overall pain and patellofemoral pain and reduced manipulation rates post-operatively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".