Mid-level constraint designs for total knee arthroplasty (TKA) are intended to reduce coronal plane laxity. Our aims were to compare kinematics and ligament forces of the Zimmer Biomet Persona posterior-stabilized (PS) and mid-level designs in the coronal, sagittal, and axial planes under loads simulating clinical exams of the knee in a cadaver model. We performed TKA on eight cadaveric knees and loaded them using a robotic manipulator. We tested both PS and mid-level designs under loads simulating clinical exams via applied varus and valgus moments, internal-external (IE) rotation moments, and anteroposterior forces at 0°, 30°, and 90° of flexion. We measured the resulting tibiofemoral angulations and translations. We also quantified the forces carried by the medial and lateral collateral ligaments (MCL/LCL) via serial sectioning of these structures and use of the principle of superposition.Aims
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There are concerns regarding complications and longevity of total elbow arthroplasty (TEA) in young patients, and the few previous publications are mainly limited to reports on linked elbow devices. We investigated the clinical outcome of unlinked TEA for patients aged less than 50 years with rheumatoid arthritis (RA). We retrospectively reviewed the records of 26 elbows of 21 patients with RA who were aged less than 50 years who underwent primary TEA with an unlinked elbow prosthesis. The mean patient age was 46 years (35 to 49), and the mean follow-up period was 13.6 years (6 to 27). Outcome measures included pain, range of motion, Mayo Elbow Performance Score (MEPS), radiological evaluation for radiolucent line and loosening, complications, and revision surgery with or without implant removal.Aims
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The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
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