American Academy of Orthopaedic Surgeons

The U.S. Centers for Medicare & Medicaid Services (CMS) has set the substantial clinical benefit (SCB) for the Knee injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS-JR) after primary total knee arthroplasty (TKA) at 20 points. We aimed to determine the percentages of patients who achieved the minimal clinically important difference (MCID) and the SCB for KOOS-JR at 1 year following TKA and to evaluate factors associated with benchmark achievement. We queried the American Joint Replacement Registry (AJRR) and screened 1,284,404 primary TKA cases performed from 2018 to 2023. We determined attainment of the KOOS-JR distribution-based MCID (7.5), anchor-based MCID (14), and SCB (20) at 12 months by each patient. Associations of covariates with the achievement of the MCIDs and the SCB were evaluated using a generalized linear model for binary outcomes that accounted for clustering within institutions. Unadjusted and adjusted odds ratios (ORs) for the outcomes of interest with 95% confidence intervals (CIs) were reported. Covariates included the preoperative KOOS-JR, sex, race or ethnicity, body mass index (BMI), Charlson Comorbidity Index (CCI), fixation type, use of technology, year of the procedure, region, institution type, teaching status, and number of beds. Linked scores were recorded by 64,773 patients. The mean patient age was 68.35 ± 8.60 years, 61.29% of patients were female, and 83.52% of patients were non-Hispanic White. The KOOS-JR threshold achievement rate was 86.8% for the calculated distribution-based MCID, 76.5% for the anchor-based MCID, and 65.7% for the SCB. Patients with higher preoperative scores (adjusted OR, 0.93 [95% CI, 0.93 to 0.93]; p < 0.001), Asian patients (adjusted OR, 0.59 [95% CI, 0.46 to 0.74]; p < 0.001), Black patients (adjusted OR, 0.55 [95% CI, 0.49 to 0.62]; p < 0.001), Hispanic patients (adjusted OR, 0.71 [95% CI, 0.51 to 0.99]; p = 0.042), non-Hispanic patients of other races (adjusted OR, 0.84 [95% CI, 0.74 to 0.95]; p = 0.007), male patients (adjusted OR, 0.89 [95% CI, 0.85 to 0.94]; p < 0.001), and patients with higher BMI (adjusted OR, 0.93 [95% CI, 0.87 to 0.99]; p = 0.025) showed lower odds of achieving the SCB. A CCI of ≥5 was additionally found to be associated with lower odds of achieving the distribution-based MCID (adjusted OR, 0.89 [95% CI, 0.79 to 0.99]; p = 0.032) and anchor-based MCID (adjusted OR, 0.89 [95% CI, 0.81 to 0.97]; p = 0.012). The CMS relatively arbitrarily defined the SCB at a value that is too high for an operation that routinely yields >80% patient satisfaction. Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

This study aimed to establish consensus on the essential qualities that determine a physical therapist's readiness to serve as an effective clinical instructor (CI) and to develop a self-assessment tool for clinicians considering this role. CIs are critical in transforming student physical therapists into entry-level clinicians, yet guidance on preparing for this role remains limited. The CI's role is well-established as vital to clinical education. Although the American Physical Therapy Association offers development opportunities through its credentialed CI Program, there is no widely accepted, evidence-based tool to help clinicians assess their readiness. Current literature highlights this gap, underscoring the need for a structured, self-assessment resource to support clinicians transitioning into educational roles. A panel of 82 experts, including physical therapy educators and experienced CIs, participated in the study. Their backgrounds in clinical education and mentoring informed the consensus-building process. Using the Delphi method, the study conducted 3 rounds of expert feedback to develop and refine a checklist for CI readiness. An initial list of 14 constructs was presented, with revisions made in each round. A 75% consensus threshold determined inclusion in the final tool. The Delphi process produced the CI Readiness Tool (CIRT), a checklist of 12 essential constructs for evaluating CI readiness. These constructs reflect the competencies and attributes needed for effective clinical instruction and were refined through expert consensus. The CIRT supports clinician self-assessment and professional development in preparation for the CI role. It provides a structured, consensus-based approach to guided readiness. Despite limitations such as participant attrition during the Delphi process, the study offers a valuable framework for improving clinical education. Further research is needed to validate and expand the tool's use in various settings.

The rapid growth of electric vehicles has sparked significant interest in battery technology, particularly in monitoring the state of charge (SOC). Accurate SOC estimation is critical for safe and efficient battery operation. While various estimation methods exist, more research is needed to adapt to diverse lithium-ion battery chemistries. Deep learning (DL) has shown promise in improving SOC estimation, but selecting optimal hyperparameters remains a challenge. This paper presents an automated hyperparameter tuning method using Bayesian optimization. The model incorporates battery data like current, voltage, and temperature, along with average voltage and current, to enhance accuracy. Tested across varying temperatures, the approach shows that BiLSTM models with 70 hidden neurons achieve SOC predictions with less than 2% root mean square error, improving reliability in battery management systems.

Abstract Purpose The aims of this study are (1) to derive a set of time series features to predict the total knee contact force during gait from two ankle-mounted inertial measurement units (IMUs); and (2) to explore the best combination of PROMs and IMU-derived, biomechanical measures for the prediction of postoperative quality of life (QoL) and reduce the inconsistency between biomechanical and PROMs. Methods Synced motion capture (optical and inertial) data were collected from four healthy participants to obtain a suitable time-series feature set that relates to knee contact forces (KCF). Then, using data from 28 patients during overground walking, we generated linear and random-forest regressor models for estimating QoL. These models were evaluated using temporal cross-validation. Results Overall, features from the IMUs could predict the total KCF (R 2 &gt; 0.90, RMSE = 0.14, %BW). A machine learning model was trained on the data obtained during the recovery (up to 1 year) to predict EQ-5D-5 L score (patient QoL). The models were able to predict within a margin of 1.81–4.40 units of the actual score 95% of the time. Conclusion The presented study derives surrogate measures of total knee contact force (KCF) using ankle-mount IMUs. We identified the best combination of PROMs and IMU-derived measures for postoperative QoL at four time-points. The optimal metrics varied for each time point: OKS only, knee kinematics with OKS, KCF with OKS, and KCF only. Further data collection will be required to compile a comprehensive dataset to generate a robust predictive model.

As anatomic shoulder arthroplasty continues to increase in popularity, there will be a similar need for revising these implants to reverse total shoulder arthroplasty. To address this problem, convertible glenoid and humeral components have been developed to facilitate a less complicated, less traumatic, and bone-preserving procedure. However, convertible glenoids have a historically higher failure rate due to loosening and joint overstuffing when used for anatomic shoulder arthroplasty, and convertible humeral stems can be problematic at the time of revision and often need to be removed because of stem malposition. Despite these issues, there have been recent advances with the humeral and glenoid components which continue to make these implant options appealing and relevant. At the same time, there is a trend toward stemless arthroplasty which makes a convertible humeral stem less important due to the ease of revision from a stemless component to a stemmed reverse shoulder arthroplasty.

Retrospective observational study. To evaluate whether the combined American Spine Registry and Medicare (ASR/CMS) data yield substantially different findings versus ASR data alone with regard to key parameters such as risk stratification, complication rates, and readmission rates in lumbar surgery investigated through an analysis of 8755 spondylolisthesis cases. Medicare data correlation has been effective for determining revision rates for other procedures, such as total hip replacement. Our aim is to determine whether these findings are translatable in the realm of lumbar spinal surgery investigated through an analysis of 8755 spondylolisthesis cases. The American Spine Registry (ASR) was queried for Medicare-eligible patients who underwent lumbar spinal fusion for lumbar spondylolisthesis. This cohort was analyzed based on ASR data alone in comparison to the same patients in the combined ASR/Medicare (ASR/CMS) data set. The primary outcome of interest was readmission at 30 and 90 days postoperatively. There were 8755 Medicare-eligible cases with a diagnosis of spondylolisthesis within the ASR. The mean age was 72.7 years and 60.8% were female. Medical comorbidities were more frequently detected in the combined ASR/CMS data set, reflected by a higher mean Charlson Comorbidity Index score (3.49 vs. 3.27, P <0.001). Hospital readmission rates were significantly higher in the combined ASR/CMS data set at both 30 days (4.89% vs. 1.83%, P <0.001) and 90 days (7.68% vs. 2.66%, P <0.001), with notable increases in readmissions for infections and medical complications. Discharge disposition remained comparable across data sets, with most patients discharged to home or home health care. This study demonstrates that integrating patient-identified Medicare data with the ASR provides a more comprehensive assessment of outcomes for lumbar spinal fusion surgery as demonstrated through an analysis of 8755 spondylolisthesis cases. These findings, establish the importance of multisource data linkage to overcome the limitations of single-source registries, thereby enhancing data quality for clinical decision-making and quality improvement in spinal surgery.