Anna N. Miller

Objectives: To describe outcomes after distal humerus nonunion surgery and to identify risk factors for failure in the same population. METHODS: Design: Retrospective case series. Setting: Seven, academic, level I trauma centers. Patient selection criteria: Included were patients who underwent operative repair of a distal humerus (OTA/AO 13) nonunion and were ≥18 years old. Patients were excluded if they had clinical or serologic signs of infection prior to nonunion repair, or if their follow-up was less than 180 days and their surgery could not otherwise be classified as a success or failure. Outcome Measures and Comparisons: This study measured the rate of recalcitrant nonunion and complications following distal humerus nonunion surgery. It additionally evaluated associations between union rate and demographics, injury characteristics, and nonunion surgery characteristics. Results: Fifty-four patients with distal humerus nonunion were identified. Of the 30 included patients (mean age 47.5 (range 22-71), 18 (60%) male), 7 (23.3%) failed to unite after their index nonunion surgery at a study institution. Thirteen patients (43.3%) experienced one or more postoperative complications. While twenty-one of the 30 (70%) included patients initially sustained an open distal humerus fracture, all 7 patients (100%) who developed a recalcitrant nonunion initially sustained an open fracture. The use of bone graft or bone graft substitute relative to no graft, patient age, body mass index, smoking status, diabetes, and initial OTA/AO fracture classification were not associated with nonunion repair success rate (all p>0.05). Conclusion: This multicenter series challenged the concept that union rates are high, defined by the authors as >90%, following distal humerus nonunion surgery. Though limited by a small sample size that precluded multivariate regression, this study’s bivariate analysis suggests that an initially open distal humerus fracture may increase the risk of recalcitrant distal humerus nonunion. Modifiable risk factors were not associated with the success rate of nonunion surgery. These findings can be used to better inform patients’ expectations of results and complications following distal humerus nonunion surgery. Level of Evidence: Therapeutic level III

Automotive trauma in motor vehicle crash (MVC) occupants and pedestrians is a leading cause of injuries and death amongst children and adults in the United States (NHTSA 2024, 2025). Many of these ...

Was not provided for this article.

Introduction: The area deprivation index (ADI) is a valuable tool for assessing patient risk holistically, yet there is limited research on the link between area deprivation and patient-reported outcomes in adult orthopaedic trauma patients after surgery. This study examines the association of ADI with both objective and subjective outcomes after surgical treatment of distal femur fractures. Methods: This retrospective cohort study included patients aged 18 to 80 years who presented with traumatic distal femur fractures over 7 years and were treated surgically (OTA/AO Classification 33A, 33B, and 33C) at a level 1 trauma center. Data collected included patient demographics (age, sex, and race), body mass index, tobacco and substance use, medical comorbidities, and insurance status. Patients were grouped by ADI as either most deprived (>85th percentile) or less deprived (<85th percentile), using the Neighborhood Atlas. Patient-reported outcomes (physical function, anxiety, pain, and depression) were collected at 0 to 3, 3 to 9, and 9 to 12 months postoperatively. Objective outcomes included complications, readmissions, reoperations, and 1-year mortality. Results: A total of 107 patients (58 females, 49 males; mean age 53.8 years) were studied. The most deprived group was younger (48.2 vs. 57.9 years) and had more Black patients (62.2% vs. 14.5%) compared with the less deprived group, which was predominantly White (83.9% vs. 35.6%). Fewer in the most deprived group had private insurance (11.9% vs. 31.7%), and more were uninsured (19.1% vs. 5%). Both groups showed notable improvements in physical function, but the most deprived group reported higher anxiety and pain scores at 3 months. Anxiety equalized at 6 months, but pain remained higher at 1 year. Conclusions: Patients with higher social deprivation experienced increased pain and anxiety despite similar objective outcomes. These findings should arm orthopaedic traumatologists with more insight on caring for socially deprived patients with a more nuanced approach prioritizing their risk profiles with focus on mental health and pain management.

Motor vehicle collisions (MVCs) are a leading cause of fracture-related morbidity and healthcare cost in the United States (Peterson et al. 2021; Yuan et al. 2023). However, fracture risk following...

Background: Large language models (LLMs) are increasingly used in healthcare for patient education and clinical decision support. However, systematic benchmarking in real-world clinical contexts remains limited, particularly for high-risk conditions such as hip fractures. Objective: To evaluate and compare the performance of three state-of-the-art LLMs—DeepSeek-V3-FW, Gemini 2.0 Flash, and ChatGPT-4.5—in answering standardized patient questions on hip fracture management. Methods: Thirty standardized questions covering general knowledge, diagnosis, treatment, and rehabilitation were developed by three specialists in orthopedics and traumatology. Each LLM generated responses independently. Three experienced orthopedic surgeons assessed accuracy (4-point scale) and comprehensiveness (5-point scale). Statistical analyses included Kruskal–Wallis and chi-squared tests. Results: All models demonstrated high reliability, with 96.7% of responses rated “Good” or “Excellent” and none rated “Poor.” Mean accuracy scores were comparable across models, and comprehensiveness averaged 4.8/5. DeepSeek-V3-FW tended to provide longer, structured answers and performed best in general knowledge, while Gemini 2.0 Flash excelled in diagnosis and rehabilitation and produced the most concise responses. ChatGPT-4.5 offered shorter, conversational answers with similar accuracy and detail. Conclusions: The three LLMs showed strong capabilities in delivering accurate and comprehensive information on hip fracture care, highlighting their potential as tools for patient education and clinical support. Differences in style and domain-specific strengths suggest complementary roles. Further research is needed to validate safety and integration into clinical workflows.

Introduction: The optimal surgical approach for intraarticular calcaneal fractures remains debated. This study compares deep infection rates between the extended lateral (EL) and sinus tarsi (ST) approaches. Methods: A retrospective review was conducted of 782 patients treated surgically for OTA 82B/C calcaneal fractures across 15 institutions. Deep infection was defined as requiring surgical débridement or treatment for osteomyelitis. Risk factors were identified using univariate and multivariate analyses. Results: Of 782 fractures, 444 were treated with EL and 338 with ST. Deep infections occurred in 6.8% of EL and 3.0% of ST cases ( P = 0.017). Multivariate analysis found that surgical approach ( P = 0.03) and age ( P < 0.001) were independent risk factors. EL had more wound complications; ST had more symptomatic implants. Conclusion: The ST approach was associated with lower rates of deep infection and wound complications, even in higher-risk patients.

Distal tibia fractures occur in approximately 4% of police-reported crashes where at least 1 vehicle was towed in the U.S., and frequently result in complications like infection, nonunion, and osteoarthritis. This study used real-world crash data to train a random forest algorithm to predict distal tibia fracture types, identifying key demographic, vehicle, and crash factors contributing to the model predictions. Crash Injury Research and Engineering Network (CIREN) cases (2005-2024) with computed tomography (CT) radiology of a distal tibia fracture were identified. Frontal, non-rollover crashes with non-pregnant, non-ejected occupants older than 13 years were selected, resulting in 94 cases (90 unilateral, 2 bilateral fractures) included for analysis. Fractures were labeled by 3 trained researchers as extraarticular, partial articular, or complete articular using the AO Foundation-Orthopedic Trauma Association (AO/OTA) compendium, which has an established relationship with clinical outcomes. An orthopedic surgeon graded a subset (N = 20) for validation. CIREN injury mechanisms and contributing factors - delta-v, vehicle model year, body type, and safety restraints, as well as occupant age, sex, BMI, and comorbidities - were collected to train a random forest classifier to predict AO/OTA fracture type. SHapley Additive exPlanations were used to examine how demographic, vehicle, and crash factors influenced the probability of random forest predictions, which allowed for the association of key factors from real-world crash data with AO/OTA fracture types. The agreement between the 3 graders (Fleiss' Kappa of 0.78) and between the ground truth classifications and the orthopedic surgeon (Cohen's Kappa of 0.76) were substantial. Random forest correctly determined the AO/OTA distal tibia fracture type in 75.5% of cases. Toe-pan intrusion as a contributing factor and delta-v greater than 30 kph were associated with increased random forest prediction probability of complete articular fractures. Knee bolster airbag deployment was associated with decreased probability of partial articular fractures, often linked with rotational injury mechanisms, suggesting they induce increased mechanisms of compression. Concomitantly, the random forest found that rotational mechanisms were associated with increased probability of partial articular fractures. Despite not using radiology, the random forest predicted distal tibia fracture type with similar accuracy to human graders, demonstrating the potential to support in areas like post-crash triage. Finally, the random forest characterized underlying relationships between factors from real-world crash data and AO/OTA fracture types, demonstrating its usefulness as an explanatory model.