Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 8 appraisals
Computer assisted surgery (Abingdon, England)
DMRNet: a dynamic multi-scale residual network for Shamrock view and lumbar plexus segmentation
Lumbar plexus block (LPB) is a regional anesthesia technique widely used for hip and knee surgeries. However, despite the assistance of ultrasound guidance, the complex anatomical structure of the lumbar plexus poses significant challenges for anesthesiologists during the procedure. To accurately identify the lumbar plexus located in the posterior third of the psoas major in the Shamrock view, a deep learning-based segmentation model named DMRNet was proposed. This model is designed to precisely delineate muscles, nerves, and bony structures in Shamrock view ultrasound images. DMRNet integrates several innovative modules, including Adaptive Multi-Scale Dilated (AMD) Module that enhances the model's ability to capture multi-scale features; Dense Attention Residual (DAR) Module that adaptively selects salient feature regions; Attention-Enhanced Hybrid (AEH) Module that emphasizes critical features while suppressing irrelevant ones; and two attention mechanisms, Boundary-Aware Spatial Attention Mechanism (BASA) and the Enhanced Residual Multi-Head Attention Mechanism (ER-MHA), that improve the model's capacity to recognize complex contextual patterns. Experimental results demonstrated that DMRNet achieved a mean Intersection over Union of 0.863 and a mean Dice coefficient of 0.926 across all target structures, outperforming other state-of-the-art models. These findings suggest that DMRNet may serve as a useful assistive tool for sonoanatomical recognition in Shamrock view ultrasound images and may provide educational support for ultrasound-guided LPB training. A dynamic multi-scale residual network is proposed for lumbar lexus (LP) segmentation.Two attention mechanisms were improved, and three modules were proposed to improve downsampling, skip connection, and upsampling.We built our own Shamrock view ultrasound database containing the lumbar plexus nerves, and the Anesthesiologist physician of the anesthesiology department annotated it using Labelme.External validation was performed using the Ultrasonic Brachial Plexus Dataset (UBPD).
14 June 2026
Read appraisal →Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology
Vascularized versus nonvascularized free fibular grafts in reconstruction of post-traumatic critical long bone defects, a comparative study
BACKGROUND: Reconstruction of critical-sized long bone defects is a complex orthopedic challenge. Free fibular grafts, vascularized (FVFG) and nonvascularized (NVFG), are established reconstructive options, but comparative clinical outcomes remain uncertain. PURPOSE: To compare clinical and radiological outcomes of FVFG and NVFG in patients with post-traumatic critical bone defects more than 10 cm. METHODS: A randomized controlled trial was conducted with 50 patients assigned equally to FVFG or NVFG groups. The primary outcome was time to union, while secondary outcomes included graft hypertrophy, functional scores (DASH and LEFS), complication rates, and donor site morbidity. RESULTS: The mean time to union was 5.78 months in the FVFG group and 6.17 months in the NVFG group, showing no statistically significant difference (p = 0.447). Rates of graft hypertrophy, functional recovery, and complications were comparable between the groups. CONCLUSIONS: Both FVFG and NVFG provide effective reconstruction for critical bone defects, with nearly similar healing times and functional outcomes. NVFG may represent a less complex alternative in selected cases.
26 May 2026
Read appraisal →Journal of robotic surgery
Extended reality and emerging artificial intelligence in orthopedic surgical training: a scoping review of educational outcomes
Immersive technologies have gained increasing relevance in orthopedic surgical education; however, the scope, outcomes, and maturity of extended reality (XR) and artificial intelligence (AI) applications remain heterogeneous. To map the educational applications, learner populations, outcome measures, and research gaps related to the use of XR and emerging AI tools in orthopedic surgical training. A scoping review was conducted following Joanna Briggs Institute methodology and reported according to PRISMA-ScR guidelines. PubMed, Scopus, and ScienceDirect were searched for English and Spanish studies published between 2015 and 2025. Fifty-four studies involving 3,066 participants were included. Virtual reality (VR) was the predominant modality (83.3%), followed by augmented reality (31.4%), while AI-based applications were infrequently reported. XR-based training was most commonly evaluated in trauma surgery, arthroscopy, and arthroplasty, primarily among medical students and residents. Most studies reported improvements in simulation-based technical performance metrics, whereas evidence on clinical outcomes, long-term skill retention, and cost-effectiveness was limited. XR-particularly VR-represents the most mature immersive technology in orthopedic surgical education. AI applications remain emergent and primarily supportive. Future research should prioritize standardized outcome measures, multicenter designs, and evaluation of long-term educational and clinical impact.
25 May 2026
Read appraisal →PloS one
Comparison between two different concentrations of ropivacaine in pericapsular nerve group block for patients undergoing total hip arthroplasty: A randomized clinical trial
Effective pain control during spinal anesthesia positioning remains a clinical challenge in elderly patients undergoing total hip arthroplasty. The pericapsular nerve group block, a novel ultrasound-guided regional anesthesia technique, has shown promise for alleviating hip pain, yet the optimal concentration of local anesthetics for this approach remains unclear. In this randomized, observer-blinded clinical trial involving 102 patients, we compared the analgesic efficacy of two concentrations of ropivacaine (0.375% vs. 0.5%) administered via pericapsular nerve group block prior to spinal anesthesia. Patients receiving 0.5% ropivacaine reported significantly lower pain scores during positioning and demonstrated prolonged postoperative analgesia up to 48 hours, without a corresponding increase in adverse events. Both concentrations improved positioning quality, reduced opioid requirements, and enhanced recovery compared to controls who received standard intravenous analgesic supplementation (including preoperative midazolam and ketorolac, and postoperative sufentanil PCA), though the higher concentration yielded modest additional benefits. These findings suggest that a 0.5% ropivacaine pericapsular nerve group block may offer superior perioperative analgesia for hip arthroplasty without compromising safety, while highlighting the need to balance analgesic potency with potential motor effects. This study informs optimization of nerve block protocols to improve perioperative outcomes in a high-risk surgical population. This study informs optimization of nerve block protocols to improve perioperative outcomes in a high-risk surgical population. The trial was registered with the Chinese Clinical Trial Registry (registration number: ChiCTR2400089440).
23 May 2026
Read appraisal →Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Synthetic computed tomography from magnetic resonance imaging: An editorial on deep learning approaches for hip and knee image translation.
This editorial provides a concise overview of synthetic computed tomography (CT) generation from magnetic resonance imaging (MRI) for musculoskeletal care, with a focus on hip and knee applications. We outline why interest in this area is growing, including the potential to reduce radiation exposure, consolidate imaging into a single visit, and align measurements across modalities for preoperative planning and longitudinal follow-up. At a high level, we summarize how deep learning methods transform MRI into CT-like volumes and what that could mean for bone morphology, implant planning, and combined assessment of bone and soft tissue. We briefly introduce representative model classes, including conditional generative adversarial networks that pair realism objectives with voxelwise constraints and diffusion models that replace adversarial training with iterative denoising for stable optimization and strong anatomical fidelity. Our goal is to orient clinicians, engineers, and researchers to the key promises and open questions, and to motivate collaborative studies that test whether synthetic CT can deliver reliable, reproducible, and clinically useful information at the point of care.
4 May 2026
Read appraisal →JMIR research protocols
Integrating Gait Analysis and AI Into Knee Osteoarthritis Care: Protocol for a 3-Phase Participatory Qualitative Study
BACKGROUND: Knee osteoarthritis (OA) leads to pain, disability, and reduced quality of life. For advanced stages, knee arthroplasty surgery is the standard treatment, yet dissatisfaction and persistent mobility deficits remain common. Current surgical decision-making processes seldom incorporate objective predictors of outcomes, such as biomechanical data. Advances in computer vision, wearable sensors, and artificial intelligence now enable efficient capture and interpretation of clinically prognostic gait features in real-world settings. However, the clinical adoption of such innovations remains limited, hindered by usability challenges, misalignment with stakeholder needs, and system-level barriers. OBJECTIVE: This study aims to inform the development of a knee OA clinical decision support (CDS) tool that integrates gait analysis and artificial intelligence with clinical decision-making. More broadly, it seeks to advance a framework for participatory digital health CDS tool implementation by identifying and addressing suboptimal workflows, stakeholder priorities, and organizational challenges. METHODS: This study uses a 3-phase participatory design process, with in-depth interviews conducted in each phase to generate insights that inform subsequent stages. Phase 1 investigates perceived barriers to and facilitators of the implementation of a digital CDS tool and examines current workflow processes in knee OA management. Phase 2 focuses on identifying and defining user requirements to guide solution ideation for translational digital decision support. Phase 3 assesses user acceptance of prototyped solutions. Field observations in phase 1 and usability testing in phase 3 will supplement interview findings with real-world evidence. RESULTS: The study was funded in November 2024. Ethics approval was granted by the Nova Scotia Health Research Ethics Board on July 28, 2025. Data collection began in September 2025. As of February 2026, a total of 6 clinicians have been interviewed, with all 6 interviews transcribed and 4 coded as part of phase 1 analysis. Study completion is anticipated by August 2026. Findings will be published by January 2027. CONCLUSIONS: Our stakeholder-driven approach prioritizes both technological rigor and practical usability, addressing key human factors for successful implementation. While initially focused on Nova Scotia's orthopedic setting, this research provides a scalable framework for integrating digital decision support tools into broader clinical environments, advancing innovation in knee OA care and beyond.
16 Apr 2026
Read appraisal →Acta orthopaedica
Effect of an in-consult patient decision aid on decisional quality and involvement for patients with severe hip or knee osteoarthritis: a multicenter, cluster randomized controlled trial
BACKGROUND AND PURPOSE: Shared decision-making (SDM) is important in managing patients with severe hip and knee osteoarthritis, where treatment decisions often depend on patient preferences and values. We aimed to evaluate the effectiveness of an in-consult patient decision aid in improving decision quality and patient involvement compared with usual care. METHODS: In a cluster-randomized controlled trial, surgeons from 2 Danish orthopedic departments were randomized to use in-consult patient decision aids or provide usual care. The primary outcome was decision quality assessed using the Hip/Knee OA Decision Quality Instrument (HK-DQI). Patients who were well informed and received their preferred treatment received an Informed Patient-Centered (IPC) score of 1, indicating good decisional quality; all others scored 0. Secondary outcomes included patient-experienced engagement and involvement in decision-making, assessed using CollaboRATE and the decision-process domain of the Hip/Knee OA Decision Quality Instrument and consultation duration. Relative risks (RR) and mean differences (MD) with 95% confidence intervals (CIs) were estimated using multilevel regression models. RESULTS: 20 surgeons were randomized to either use in-consult patient decision aids (n = 10) or provide usual care (n = 10). Of the 1,038 patients screened for severe hip or knee osteoarthritis, 581 were included in the study. Decision quality was similar between groups, with a score of 43 (36-51) in the intervention group and 44 (37-52) in the usual care group (RR 0.9, CI 0.7-1.2; P = 0.8), likewise, patient-experienced engagement assessed with CollaboRATE (RR 1.1, CI 0.8-1.4; P = 0.6). Consultations were longer in the intervention group, with a mean difference of 3.9 minutes (CI 0.1-7.7; P = 0.04). CONCLUSION: Decision quality and patient-reported engagement were similar between groups, while consultation time was longer with in-consult patient decision aids use. In the absence of prespecified minimal important differences, the clinical relevance of these findings remains uncertain.
10 Apr 2026
Read appraisal →Journal of special operations medicine : a peer reviewed journal for SOF medical professionals
Combat-Related Orthopedic Trauma in the Russo-Ukrainian War: A Systematic Review
INTRODUCTION: The Russo-Ukrainian conflict has produced large numbers of military and civilian casualties, with orthopedic and musculoskeletal trauma representing a major proportion of combat injuries. Explosions, gunshots, and blast mechanisms frequently generate complex wounds that demand specialized surgical and rehabilitative management. This review summarizes injury types, mechanisms, severity, clinical responses, and gaps in current knowledge. METHODS: Following PRISMA guidelines, a comprehensive search of PubMed, Scopus, Web of Science, and MEDLINE via WoS was conducted in February 2025. Eligible studies published from 2014 to 2024 included case reports, case series, cohort studies, and observational research describing orthopedic injuries in military or civilian populations affected by the conflict. Two reviewers independently screened studies, performed quality assessments using Joanna Briggs Institute tools and STROBE criteria, and synthesized data qualitatively with descriptive statistics when available. RESULTS: Thirty-one studies were included, primarily case reports and retrospective analyses from Ukrainian military hospitals and frontline facilities. Explosive and ballistic trauma produced high-energy fractures, traumatic amputations, severe soft-tissue loss, and neurovascular damage. Management strategies included external and internal fixation, bone grafting, microsurgical reconstruction, distraction osteogenesis, and the progressive use of 3D-printed implants to support limb salvage. Rehabilitation, including pain control, physiotherapy, and prosthetic training, was essential for functional recovery. Long-term complications included neuromas, phantom pain, stump morbidity, and psychological trauma. Telemedicine enabled remote consultation and continuity of care under resource-limited conditions. CONCLUSIONS: Combat-related orthopedic injuries in this conflict require advanced surgical techniques, coordinated multidisciplinary support, and prolonged rehabilitation. Expanded data collection, standardized reporting, and long-term follow-up are essential to improve functional outcomes and reintegration.
4 Apr 2026
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