Purpose: New alignment classifications based on phenotype reproduction have recently been introduced in total knee arthroplasty (TKA) as alternatives to traditional mechanical alignment. These classifications were designed according to the static hip–knee–ankle angle (sHKA) measurement from long leg radiographs (LLRs). This study aimed to understand whether and how the HKA varied throughout the knee's range of motion (ROM) during robot‐assisted TKA. Methods: This prospective, bi‐centric cohort study involved 107 consecutive patients undergoing primary robot‐assisted TKA. The surgical technique adhered to restricted kinematic alignment (HKA ± 3°) with asymmetric gap balancing principles. The HKA's dynamic variation (dHKA) was assessed intraoperatively at full extension, as well as at 30°, 45°, 60°, 90° and 120°, both before bone cuts and after the positioning of the trial components. The overall cohort was initially analyzed, followed by a subgroup analysis based on varus, neutral and valgus phenotypes. A descriptive analysis was conducted to evaluate dHKA trends. Collected data were then analyzed using one‐way repeated measures analysis of variance with Bonferroni correction and Bland–Altman plots to assess significant variations in dHKA across the ROM during flexion and to quantify outliers from the established safe boundaries of ±3°. Results: Out of 107 knees, the pre‐cut dHKA demonstrated a biphasic trend, decreasing in varus until 60° and then transitioning toward valgus, with significant differences primarily noted at 90° and 120°. Post‐cut, the dHKA exhibited an overall varus trend, increasing from full extension to 60° before experiencing a partial recovery. Significant differences were detected primarily at the initial flexion angles. Outlier rates increased with flexion: pre‐cut from 6.5% to 43.0%, and post‐cut from 1.9% to 30.8%, highlighting progressive inter‐individual variability throughout. Although the analysis was stratified by knee phenotype, the post‐cut dHKA trend did not differ among the various phenotypes or in comparison to the overall cohort trend. Conclusions: The main finding of the current study was that intraoperative dHKA differs significantly from sHKA during robot‐assisted TKA. Moreover, the sHKA was limited in predicting the actual kinematic HKA. Planning the final TKA alignment on static, standing LLRs may have limited value compared to intraoperative planning conducted with enabling technologies. Level of Evidence: Level 3.

Objective: Three computed tomography (CT)-based classifications, including Haraguchi, Bartoníček–Rammelt, and Mason–Molloy systems, have been developed to better determine the characteristics of posterior malleolus fractures (PMFs). The aims of this study were (1) to determine the intra- and inter-observer reliability of the computed tomography-based classification systems and (2) to investigate the clinical experience and expertise on their reliabilities. Methods: Sixty-seven preoperative ankle CT scans of 67 adult patients with “ankle fracture with the involvement of the posterior malleolus”’’ were retrospectively identified. CT images were assessed by 10 observers with different levels of clinical experience from 2 different specialties, including orthopedics and radiology. The observers were asked to classify PMFs according to 3 CT-based classifications on 2 separate occasions with a 4-week interval. Cohen’s κ values were measured for 2 raters and Fleiss’ κ values were measured for 3 raters and more. Results: Overall, each classification had moderate to very good intraobserver reliability (κ=0.41 to 0.95, P < .01) as well as moderate interobserver reliability for each of the 2 separate assessments (κ=0.41 to 0.60, P < .01 for the first occasion; κ=0.44 to 0.59, P < .01 for the second occasion). Interobserver agreement among the foot and ankle surgeons regarding the Haraguchi and Bartoníček classifications was substantial for both assessment periods, whereas there was a moderate agreement for the Mason classification. Orthopedic residents showed moderate interobserver agreement in each period for both Bartoníček and Mason classifications. Radiology experts illustrated slight and fair agreements in the 2 assessments for Mason classification, moderate agreement in both assessments for Haraguchi classification, and substantial to moderate agreement in the first and second assessments for Bartoníček classification, respectively. Conclusion: Computed tomography-based classification system for PMFs demonstrated moderate interobserver reliability as well as moderate to very good intraobserver reliability. Moreover, foot and ankle specialists exhibit enhanced K values for both inter and intraobserver reliability for each classification system, consistency seems to increase as the interest in the field condenses. Level of Evidence: Level III, diagnostic study.

Background: Culture-negative periprosthetic joint infections (PJIs) are dramatically increasing in prevalence. The success rate of implant-saving procedures in acute PJI cases is closely correlated with prompt diagnosis, rapid isolation/identification of the microorganism, and timely surgical intervention. Methods: A 70-year-old female patient with an acutely infected left total hip arthroplasty (THA) following a routine screening colonoscopy was rapidly treated with debridement, antibiotic pearls, and retention of the implant (DAPRI) after rapid identification of the microorganism using a molecular diagnostics-based algorithm. Results: Molecular diagnostics enabled the identification of Escherichia coli as the causative agent of the transient bacteremia and subsequent seeding of the left hip within less than an hour. Conclusions: This case suggests that endoscopic procedures may increase the risk to joint replacement patients. In acute PJI, the use of molecular diagnostics, which facilitates prompt identification of microorganisms, may increase the success rate of implant-saving surgical procedures.

Purpose: Modern personalised total knee arthroplasty (TKA) techniques aim to mimic natural knee kinematics by focusing on soft‐tissue dynamics rather than static alignment. However, a consistent definition of the optimal ligamentous balance has not yet been established. This study had three aims: to assess intraoperative collateral ligament tension and the overall balance between the medial and lateral compartments; to evaluate postoperative gait analysis parameters and compare them with the healthy contralateral knee; and to determine whether the achieved intercompartmental balance affects the final coronal alignment of the knee. Methods: A prospective single‐cohort study was conducted to evaluate 102 primary AR‐assisted TKAs performed for end‐stage unilateral knee osteoarthritis between September 2021 and June 2024. A total of 17 patients out of 102 were ultimately included following the application of the study's inclusion and exclusion criteria. Intraoperative data were collected on MCL and LCL elongation patterns during passive ROM both before and after trial component placement. Additionally, the study examined differences in intercompartmental ligament balancing. Postoperative coronal alignment was radiographically evaluated using the hip‐knee‐ankle (HKA) angle. Functional outcomes were measured using 3D markerless gait analysis at least 9 months after surgery. A repeated‐measures analysis of variance (ANOVA) and a Pearson correlation were employed to examine trends in ligament asymmetry and their relationship with alignment. Results: The medial collateral ligament (MCL) exhibited nearly isometric behaviour throughout the range of motion (0.3 ± 1.11 at 0°, −2.4 ± 2.71 at 20°, −2.3 ± 3.96 at 40°, −2.3 ± 4.16 at 60°, −1.5 ± 4.03 at 80°, −0.7 ± 4.52 at 100°, −0.2 ± 4.97 at 120° preoperatively; 2.2 ± 2.98 at 0°, 2.1 ± 3.40 at 20°, 1.4 ± 4.02 at 40°, 1.2 ± 4.41 at 60°, 0.8 ± 4.48 at 80°, 1.5 ± 4.42 at 100°, 1.6 ± 5.09 at 120° postoperatively). Conversely, the LCL showed progressive detensioning (0.0 ± 2.03 at 0°, −3.1 ± 2.07 at 20°, −3.9 ± 2.72 at 40°, −4.9 ± 3.34 at 60°, −6.6 ± 3.63 at 80°, −8.5 ± 4.13 at 100°, −9.6 ± 4.40 at 120° preoperatively; −2.1 ± 5.16 at 0°, −3.1 ± 4.03 at 20°, −4.7 ± 3.80 at 40°, −5.8 ± 3.19 at 60°, −7.0 ± 3.86 at 80°, −6.6 ± 4.55 at 100°, −7.0 ± 5.08 at 120° postoperatively), leading to asymmetric ligament balancing with the opening of the lateral compartment. This trapezoidal soft‐tissue configuration persisted postoperatively. No significant differences were observed in gait parameters between the operated and contralateral limbs, all p ‐values being > 0.05. A notable negative correlation was seen between postoperative HKA and ligament asymmetry at early flexion angles (0°, 20°, 40°; r = −0.63, r = −0.67, r = −0.48, respectively), indicating that soft‐tissue balancing affects coronal alignment. Conclusions: Focusing on dynamic soft‐tissue boundaries rather than static alignment targets may preserve knee kinematics after TKA. Augmented reality‐assisted balancing techniques provide real‐time, personalised ligament management, which aligns with the patient's native kinematics and could be an encouraging direction in modern knee arthroplasty. More research is needed to confirm these findings across larger groups and different technologies. Level of Evidence: Level III.