A Retrospective Comparison of a Medial Pivot and Posterior-Stabilized Total Knee Arthroplasty With Respect to Patient-Reported and Radiographic Outcomes
Bibliographic record
Abstract
BACKGROUND: Medial pivot (MP) type total knee arthroplasty (TKA) implants are designed with a highly congruent medial tibiofemoral articulation. Compared with the cam-and-post design of the posterior-stabilized (PS) TKA, the MP-TKA design has been hypothesized to better replicate the natural kinematics of the knee. We compared the MP-TKA and PS-TKA designs, with our primary outcome measures being range of motion (ROM) and patient-reported satisfaction. METHODS: This study was a retrospective comparison between the 2 groups (76 MP-TKA vs 88 PS-TKA). ROM was collected preoperatively, 6 weeks, 6 months, and 1 year postoperatively. The Forgotten Joint Score-12 (FJS-12) scores were collected at a minimum of 1 year postoperatively. RESULTS: There was no statistically significant difference in age, gender, or body mass index between the groups. We found a statistical difference in preoperative ROM (MP = 120.3°, PS = 112.8°, P = .002). There was no difference in ΔROM at 6 weeks (MP = -12.36, PS = -3.79, P = .066), 6 months (MP = -4.23, PS = 2.73, P = .182), or 1 year (MP = .17, PS = 3.31, P = .499). Patients who underwent the MP-TKA scored significantly better than the PS-TKA on the FJS-12 score (MP = 59.72, PS = 44.77, P = .007). CONCLUSION: We found that patients who underwent the MP-TKA scored better on the FJS than those who underwent the PS-TKA; particularly with regard to deep knee flexion and stability of the prosthesis. The MP-TKA design may offer improved patient outcomes because of its highly congruent medial tibiofemoral articulation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".