Citations

Introduction Out‐of‐hospital community births account for 1.6% to 2% of births in the United States and are associated with fewer obstetric interventions but increased neonatal risk. Little is known about how maternity care providers’ characteristics, values, and prior exposure to community birth influence attitudes toward community birth. Methods We conducted an observational cross‐sectional survey using a 40 to 43 item Research Electronic Data Capture questionnaire distributed October 2 to 30, 2024. Eligible participants (“providers”) included in‐hospital physicians, midwives, community birth providers, and registered nurses. The survey assessed demographics, practice characteristics, provider values, prior community birth experience, and attitudes toward community birth. Descriptive statistics were used to summarize characteristics. Group comparisons between in‐hospital and community birth providers were conducted using analysis of variance and χ 2 or Fisher exact tests. Associations between attitudes and provider values were evaluated using linear models with Bonferroni correction. Results Sixty‐seven providers completed the survey (51 in hospital, 16 community based). Most respondents were female (89.6%) and White (77.6%), with a median of 7 years in practice (IQR, 3‐14.5). Attitudes significantly differed by role ( P = .007) and credentials ( P = .002). In‐hospital providers reported safety concerns about community birth and perceived antenatal counseling to be inadequate. Perceived patient distrust during transfer was common among both in‐hospital and community‐based providers (69% and 81%, respectively). After adjustment for provider role and multiple comparisons, no significant differences in personal values were noted by attitude group; however, providers with positive attitudes toward community birth were more likely to believe that patients value compassion (β = 2.3, P = .004). Discussion Provider attitudes toward community birth appear to be shaped more by professional role and clinical experience than by differences in underlying value systems. High levels of perceived patient distrust during transfer highlight the need for interventions that strengthen compassion across maternity care settings.

Background In 2019, the “Facts on Hand” direct-to-consumer advertising (DTCA) campaign, featuring former NFL quarterback John Elway, was launched to raise awareness of Dupuytren's contracture (DC) and its nonsurgical treatment options. Although DTCA has been shown to influence health care behavior in other specialties, its impact on treatment patterns for hand conditions remains unclear. Methods Google Trends was used to examine the influence of direct-to-consumer advertising (DTCA) on Internet-based information-seeking behavior related to DC. Changes in the proportion of surgical and nonsurgical management of DC were evaluated using 2016–2021 Medicare provider billing claims. Results Search interest in DC peaked during the campaign period, with breakout-level increases (>5000%) for terms such as “John Elway,” “needle aponeurotomy,” and “hand surgeon near me.” Medicare data revealed regionally significant increases in CCH utilization, particularly in the Midwest (+10%) and Northeast (+5%), with corresponding declines in fasciectomy. Regional overlap between increased search activity and shifts in treatment patterns suggests a geographically reinforced campaign effect. Conclusion The “Facts on Hand” campaign was associated with increased public interest in DC and coincided with regionally specific shifts in treatment utilization. Although the campaign emphasized a nonsurgical option, breakout searches for terms such as “hand surgeon near me” and “Dupuytren's surgery” suggest that treatment-specific advertising may have encouraged broader engagement with treatment overall. These findings highlight the potential for DTCA to not only raise awareness of the condition itself but also prompt patients to seek specialist care.

Abstract Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are increasingly used for weight management and cardiometabolic health. Common peroneal neuropathy (CPN) associated with rapid weight loss is attributed to adipose tissue reduction and subsequent nerve compression at the fibular head. Cases of “Slimmer’s Palsy” have been described in conditions involving rapid weight loss, including anorexia, malignancy, and post-biliary surgery, yet it remains underrecognized as a potential complication of GLP-1RA therapy. This case report describes two nondiabetic patients who developed acute foot drop after losing 14% and 18% of their total body weight over 3–6 months of semaglutide and tirzepatide use, respectively. As use of GLP-1RAs continues to rise, peripheral nerve surgeons should be aware of Slimmer's Palsy as a predictable and treatable complication of rapid weight loss and be prepared to intervene before permanent denervation occurs.

Background: Documentation burden is a major contributor to surgeon burnout, particularly in high-volume outpatient specialties such as hand surgery. These encounters often involve multiple concurrent diagnoses, procedural planning, and longitudinal care. Ambient artificial intelligence (AI) tools, which passively transcribe and summarize clinical encounters in real time, have the potential to streamline note generation and reduce cognitive load. Methods: Following institutional rollout of a commercially available ambient AI tool in early 2025, the system was adopted across all outpatient visits in a hand surgery clinic. Observations were collected regarding workflow adaptation, documentation quality, and perceived cognitive impact. To assess note characteristics, 14 carpal tunnel syndrome visit notes (7 AI-generated and 7 surgeon-written) were compared by word count, Flesch-Kincaid readability score, and inclusion of social and medical history. Results: AI-generated histories were longer and exhibited greater linguistic complexity compared with surgeon-written notes. Surgeon-written assessments were longer, more readable, and more consistent in structure and tone. Social history was documented more frequently in AI-generated notes. All AI-generated assessment and plan sections followed a structured, problem-based format. Conclusions: Ambient AI documentation can support the creation of more structured and contextually rich clinical notes in hand surgery settings. However, successful integration requires active clinician engagement and workflow alignment to balance efficiency with clinical accuracy. These findings highlight both the promise and the practical considerations of incorporating ambient AI into surgical documentation workflows.

Background: Diabetic patients experience carpal tunnel syndrome (CTS) at high rates, have increased symptom severity, and experience prolonged recovery after carpal tunnel release (CTR). This study assessed the association of glucagon-like peptide-1 receptor agonist (GLP-1) therapy with postoperative outcomes after CTR in type II diabetic patients with CTS. Methods: Using the PearlDiver database, 25 229 diabetic patients with CTS who underwent CTR and were prescribed GLP-1 therapy were identified and exact matched to 25 229 controls based on age, sex, Elixhauser comorbidity index, tobacco use, obesity, hypothyroidism, and body mass index where available. Demographic variables and comorbidities were compared between groups using Kruskal-Wallis analyses for continuous variables and chi-square tests for categorical variables. Bivariate logistic regression analysis was conducted for 90-day outcomes and 2-year CTR revision. Results: Patients on GLP-1 therapy had decreased risk of 90-day medical complications (odds ratio [OR] = 0.90; 95% confidence interval [CI], 0.84-0.96) and hospital readmission (OR = 0.85; 95% CI, 0.77-0.94) of 0.5% and 0.4%, respectively, compared with controls. No significant differences were found in 90-day surgical complications or 2-year revision rates between cohorts. There were no cases of aspiration pneumonia. The GLP-1 cohort had increased odds of higher 90-day total costs of care (OR = 2.42; 95% CI, 1.88-3.15). Conclusions: GLP-1 therapy is associated with reduced risk of 90-day medical complications and hospital readmission following CTR in diabetic patients, suggesting these medications may optimize comorbidity profiles via glucose control preoperatively. Future studies may examine whether GLP-1 therapy is associated with minimum clinically important difference in patient-reported outcome measures for CTS in diabetic patients.

Background: Artificial intelligence (AI) is a promising tool to aid in diagnostic accuracy and patient communication. Prior literature has shown that ChatGPT answers medical questions and can accurately diagnose surgical conditions. The purpose of this study was to determine the accuracy of ChatGPT 4.0 in evaluating radiologic imaging of common orthopedic upper extremity bony pathologies, including identifying the imaging modality and diagnostic accuracy. Methods: Diagnostic imaging was sourced from an open-source radiology database for 6 common upper extremity bony pathologies: distal radius fracture (DRF), metacarpal fracture (MFX), carpometacarpal osteoarthritis (CMC), humerus fracture (HFX), scaphoid fracture (SFX), and scaphoid nonunion (SN). X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) modalities were included. Fifty images were randomly selected from each pathology where possible. Images were uploaded to ChatGPT 4.0 and queried for imaging modality, laterality, and diagnosis. Each image query was completed in a new ChatGPT search tab. Multinomial linear regression was used to identify variations in ChatGPT’s diagnostic accuracy across imaging modalities and medical conditions. Results: Overall, ChatGPT provided a diagnosis for 52% of images, with accuracy ranging from 0% to 55%. Diagnostic accuracy was significantly lower for SFX and MFX relative to HFX. ChatGPT was significantly less likely to provide a diagnosis for MRI relative to CT. Diagnostic accuracy ranged from 0% to 40% with regard to imaging modality (x-ray, CT, MRI) though this difference was not statistically significant. Conclusions: ChatGPT’s accuracy varied significantly between conditions and imaging modalities, though its iterative learning capabilities suggest potential for future diagnostic utility within hand surgery.