Background/Objectives: Wearable activity monitors and sensor-based devices are increasingly used to quantify mobility, load, and recovery in orthopaedic patients, yet clinicians lack practical guidance on selection, implementation, and interpretation. This qualitative expert consensus study synthesized real-world experiences from leaders in orthopaedics, rehabilitation, biomechanics, and digital health who implemented wearables at scale. Methods: Semi-structured interviews were conducted with 16 experts (64% response rate) recruited via hybrid purposive and snowball sampling. Participants included orthopaedic surgeons and research scientists with 124 cumulative years of wearable experience across over 9000 monitored patients. Interviews addressed device selection, clinical workflow, data management, and adoption barriers. Data were charted into a structured extraction matrix and analyzed using Inductive Thematic Analysis and a Framework Approach, reported per COREQ guidelines. Results: Experts utilized diverse sensor platforms across arthroplasty, trauma, spine, and sports medicine. Four key themes emerged: (1) device selection prioritized usability and patient compliance over technical sophistication; (2) workflow required defined team roles to manage data volume and avoid clinical burden; (3) patient engagement favored simplified, actionable feedback amid divergent views on data transparency; (4) future outlook anticipated AI-driven proactive risk prediction. Conclusions: No single wearable suits all orthopaedic practices; success hinges on aligning sensor placement with clinical questions, rigorous data quality checks, and integration into care plans. This study offers a practical checklist and roadmap for point-of-care adoption.

Introduction: The AO/OTA Fracture and Dislocation Compendium is important in clinical communication and is critical for research, yet it is rarely documented systematically in electronic medical records. This study evaluates the accuracy of a Retrieval-Augmented Generation (RAG) Large Language Model (LLM) pipeline in automating AO/OTA fracture classification from plain-text radiology reports and clinical communication via verbal presentations during morning fracture conferences. Methods: We collected 71 de-identified radiology reports from a Level 1 trauma center. An expert OTA Classification Committee of eight attending surgeons established three "ground truths": one based on report text, one based on radiographs, and one based on resident verbal presentations. A HIPAA-compliant LLM (Versa, based on GPT-o3) was integrated with a RAG pipeline using the AO/OTA Compendium as its reference library. The LLM’s performance was compared against the committee's labels and a baseline cosine similarity NLP method using Accuracy and Cohen’s Kappa. Results: The RAG-augmented LLM demonstrated Near Perfect agreement for foundational categories, including Bone (97.6%, kappa=0.972) and Location (95.1%, kappa=0.938). For core morphology, the model reached Moderate agreement for Type (68.3%, kappa=0.579) and Fair for Group (62.2%, kappa=0.396). Cumulatively, the LLM maintained Substantial agreement through the Bone/Location/Type level (65.9%, kappa=0.645), significantly outperforming the baseline NLP method (22.5%, kappa=0.214). At the Bone/Location level, LLM agreement with imaging ground truth (81.7%) approached the committee's own inter-rater consistency (87.8%). When applied to resident verbal presentations during fracture conference, the LLM achieved Substantial agreement for Bone (78.6%, kappa = 0.758) and Location (72.9%, kappa = 0.648), and Fair agreement at the Bone/Location/Type level (41.4%, kappa = 0.403). Conclusion: A RAG-enhanced LLM can classify fractures from plain text and verbal clinical communication with reliability comparable to expert surgeons, both from structured radiology reports and unstructured resident verbal presentations. While manual refinement is still required for granular universal modifiers, the substantial agreement at the Bone/Location/Type level supports the implementation of AI-assisted documentation tools. Such tools offer a lightweight, scalable solution to enhance EMR data quality and facilitate large-scale orthopaedic research. Further integration of LLMs into clinical workflows is needed to provide real-time feedback that improves radiology reporting and clinical communication.

UNSTRUCTURED Musculoskeletal health literacy requires patients to understand complex treatment options, postoperative precautions, and recovery timelines, which together help set realistic expectations for recovery. However, existing patient education materials are often text-heavy, exceed recommended reading levels, and fail to depict how functional recovery progresses over time, which may be especially limiting in safety-net settings serving populations with variable health literacy. In this paper, we describe methods for designing and developing a secure, institution-restricted gait-recovery video library for orthopedic patient education. Our library was built to close that gap with short, patient-perspective recovery videos centered on one of the most meaningful outcomes of lower-extremity surgery: functional mobility. Each video was built around a standardized Timed Up and Go (TUG) assessment recorded from frontal and sagittal views, paired with relevant radiographs and visually adapted patient-reported outcome measures (PROM), to create a multimodal, visually guided recovery pathway. This publication aims to detail the process of selecting a secure hosting platform; choosing the filming setup, recovery milestones, and key visual features to capture; maintaining patient privacy and data security; executing clinic-based filming and video editing; and building a personalized interface that allows videos to be filtered by procedure type, recovery stage, and patient characteristics.

Purpose : This systematic review and meta-analysis compares incidental dural tear rates between uniportal and biportal endoscopic lumbar surgeries. Uniportal and biportal techniques represent established minimally invasive approaches, though differential risks for complications such as dural tears warrant thorough investigation. Methods : A comprehensive literature search across Google Scholar, PubMed/MEDLINE, and Cochrane Library identified 67 studies, encompassing 10,491 patients (4,657 biportal, 5,834 uniportal). Results : Initial pooled analysis indicated a higher dural tear incidence in the biportal group (3.16%, 152/4,657) compared to the uniportal group (1.69%, 100/5,834), a statistically significant difference (p < 0.001). However, a more focused meta-analysis of 25 studies revealed no statistically significant difference in pooled dural tear incidences (uniportal: 1.4% [95% CI: 0.3-2.4%] vs. biportal: 1.1% [95% CI: 0.6-1.7%], p = 0.42). Secondary outcomes showed biportal endoscopy associated with slightly shorter operative times, while uniportal surgery correlated with shorter hospital stays. Both approaches yielded comparable functional outcomes. Conclusion : While initial data suggested a difference, the meta-analysis found no significant disparity in dural tear risk. Optimal technique selection, considering patient and surgical factors, remains important for minimizing complications.