Olmsted Medical Center

Developing high-quality multiple-choice questions (MCQs) for medical education is a challenging and time-consuming task. This study aimed to assess the applicability of Chat Generative Pre-trained Transformer (ChatGPT) in generating MCQs for ophthalmology residents. ChatGPT 4 was used to generate 100 MCQs, while an additional 100 MCQs were authored by university faculty. Item-writing flaws in both sets of questions were evaluated by a single reviewer. A quality assessment panel, consisting of board-certified ophthalmology subspecialists, compared the quality of two sets. Ophthalmology residents then answered all MCQs in a randomised order. The item difficulty and discrimination indices were calculated and compared between the two sets of questions. Item-writing flaws were more frequent in ChatGPT-generated MCQs (56%) compared with human-authored MCQs (27%, p<0.001). While ChatGPT-generated questions were comparable to human-written ones in most quality parameters, the distractor quality was significantly higher in human-generated MCQs (p=0.006). The mean resident scores were 46.5±9.5 for the ChatGPT-generated MCQs and 49.0±10.9 for the human-written MCQs (p=0.051). The difficulty index was 0.47±0.21 and 0.51±0.19, respectively (p=0.12). The discrimination index was significantly lower in the ChatGPT questions (0.20±0.19 vs 0.28±0.16, p<0.001). While ChatGPT can efficiently generate MCQs for ophthalmology residents, it has notable limitations, including higher rates of item-writing flaws and lower-quality distractors. Additionally, ChatGPT-generated MCQs are less effective at distinguishing high-performing from low-performing examinees. Integrating ChatGPT with human expertise is essential to enhance the quality and reliability of artificial intelligence-generated MCQs.

To investigate the incidence and risk factors of intraoperative complications during phacoemulsification surgery performed by ophthalmology residents at an academic training center. This comparative case series included 1121 eyes of 821 patients that underwent phacoemulsification cataract surgery by ophthalmology residents from March 2020 to March 2021. Patients' medical records were reviewed for demographics, systemic and ophthalmic comorbidities, biometric data, surgical details, anesthesia type, residents' training year, and the occurrence of any intraoperative complications. Multivariate models were used to identify potential risk factors for intraoperative complications. Intraoperative complications were observed in 63 eyes (5.6%). From the most to the least frequent, they included posterior capsular rupture with vitreous loss in 29 eyes (2.6%), anterior capsular tear in 21 eyes (1.9%), nucleus drop in 8 eyes (0.7%), zonular dehiscence in 3 eyes (0.3%), and posterior capsular rupture without vitreous loss in 2 eyes (0.2%). On univariate analysis, mature or brunescent cataracts (OR = 3.096, P = 0.031), residents' training year (OR = 2.238, P = 0.017), and pseudoexfoliation (OR = 2.247, P = 0.049) were associated with vitreous loss. Multivariate data analysis indicated that mature or brunescent cataracts (OR = 4.046, P = 0.009) and residents' training year (OR = 3.238, P = 0.005) were independent risk factors for vitreous loss. We observed a higher rate of intraoperative complications in eyes with mature or brunescent cataracts or in procedures performed by less experienced residents. Proper case selection and direct attending supervision are crucial in preventing intraoperative complications at ophthalmology training centers.

The LunaNet Interoperability Specification (LNIS) has been created and released to the public, enabling international commercial and government lunar systems to have a common baseline to work together in forming complex lunar mission networks. The LNIS specifically defines a set of data service protocols that offer multiple options for different types of network users and providers to work together. This includes real-time frame delivery services similar to those classically used by missions, as well as both real-time and store-and-forward networking that will be important as the number of mission users and complexity of lunar mission operations increases. The NASA Lunar Communications Relay and Navigation Systems (LCRNS) project requires lunar relay satellite services to implement onboard processing and networking capabilities in some ways similar to LEO mega-constellations that offer Internet access, but also going beyond those capabilities to meet unique lunar mission needs (e.g. store-and-forward services, LNIS messaging, etc.). Onboard processing will be necessary for a number of different protocols that are included in the LNIS, including support for data transfer over lunar proximity radio links based on CCSDS framing and CCSDS Encapsulation Packets, networking via IP and DTN Bundle Protocol, a suite of DTN Convergence Layer Adapters, and LunaNet messaging services. This paper provides an overview of our work in contributing to and defining the LNIS service interfaces and LCRNS requirements related to onboard processing for data services. This includes exploration of the key aspects of lunar networking concept of operations, technology trade studies, protocols for trunking, applicability and scope of different protocols, messaging, security considerations, and realistic implementation and deployment constraints. While the technology for new lunar relay networks can build upon the recent advances in LEO constellations, there are unique new challenges related to network management, onboard processing hardware/software system constraints, security, and quality of service for human spaceflight missions. As multiple international systems are planned to be put online in the coming years based on the LNIS and derived data services, this work will have a lasting impact that supports the evolution from single-vehicle user missions into scenarios where many surface and orbital assets are able to collaborate over multi-hop networks operated by diverse providers.

To identify characteristics associated with a higher likelihood of patient-initiated encounters with a health care professional before the scheduled 6-week postpartum visit. We performed a retrospective cohort study of postpartum persons who received prenatal care and delivered at a single academic level IV maternity care center in 2019. We determined associations between maternal sociodemographic and obstetric characteristics and the likelihood of patient-initiated early postpartum encounters with χ2 tests for categorical variables and Wilcoxon rank sum tests for continuous and ordinal variables. A total of 796 patients were included in our analysis, and 324 (40.7%) initiated an early postpartum encounter. Significantly more postpartum persons who initiated early postpartum encounters were primiparous persons (54.3%) than multiparous (33.8%) persons (P < .001). Postpartum persons who desired breastfeeding or who had prolonged maternal hospitalization, episiotomy, or cesarean or operative vaginal delivery were also significantly more likely to initiate early postpartum encounters (all P≤.002). Of postpartum persons who initiated early encounters, 44 (13.6%) initiated in-person visits, 138 (42.6%) initiated telephone or patient portal communication, and 142 (43.8%) initiated encounters of both types. Specifically, 39.2% of postpartum persons initiated at least one early postpartum encounter for lactation support, and nearly half of early postpartum encounters occurred during the first week after hospital discharge. Early postpartum encounters were more common among primiparas and postpartum persons who were breastfeeding or had prolonged hospitalization, episiotomy, cesarean delivery, or operative vaginal delivery. Future studies should focus on the development of evidence-based guidelines for recommending early postpartum visits.

Determine the risk and etiology of epilepsy among systemic autoimmune conditions including rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE)

PurposeTo investigate the effect of static accommodative tasks on intraocular pressure (IOP) of glaucomatous and normal eyes.MethodsFour groups of subjects categorized as primary open-angle glaucoma (POAG), primary angle-closure suspects (PACS), normal age-matched controls, and normal young adults (NYA; age 40 years) were enrolled. The baseline IOPs were measured after the subjects were looking at a distant target for 15 min. Static accommodation was obtained by execution of near vision tasks (reading at 33 cm in daylight [300 lux] for 60 min). IOPs were measured at 15, 30, 45, and 60 min intervals while accommodating and then measured again after 15 min of relaxing accommodation while looking at a distant target.ResultsOne-hundred and eighteen eyes of 98 subjects were recruited. The study groups consisted of the following categories: 25 POAG (46 eyes), 24 PACS (47 eyes), 25 matched controls (50 eyes), and 24 NYA (48 eyes). Within all groups, the mean IOP decreased throughout the accommodation period at all time points. Maximum IOP reduction after accommodation was detected at the 30-min time among the POAG subjects, at the 45-min time in the PACS and matched control groups, and at 15 min after the relaxation of accommodation in the NYA group. IOP reduction levels showed no statistically significant difference among POAG, PACS, and the normal matched groups in their response to accommodation. However, NYA had significantly lower IOP and greater IOP reduction after the resting period (relaxation of accommodation).ConclusionStatic accommodative tasks can significantly reduce IOP in normal, POAG, and PACS individuals. Encouraging glaucoma patients to practice periodical near vision tasks could be viewed as an adjunctive measure for glaucoma management.

Background We investigated the longitudinal association of herpes zoster (HZ), commonly known as "shingles," and long-term risk of stroke or coronary heart disease (CHD) among participants in 3 large US cohorts, the NHS (Nurses' Health Study), NHS II (Nurses' Health Study II), and HPFS (Health Professionals Follow-Up Study). Methods and Results Participants were 79 658 women in the NHS (2000-2016), 93 932 women in the NHS II (2001-2017), and 31 440 men in the HPFS (2004-2016), without prior stroke or CHD. Information on HZ, stroke, and CHD was collected on biennial questionnaires and confirmed by medical record review. Cox proportional hazards regression models were used to estimate multivariable-adjusted hazard ratios for stroke and for CHD according to years since HZ compared with never HZ. During >2million person-years of follow-up, 3603 incident stroke and 8620 incident CHD cases were documented. History of HZ was significantly and independently associated with higher long-term risk of stroke and CHD. In pooled analyses, compared with individuals with no history of HZ, the multivariable-adjusted hazard ratios (95% CIs) for stroke were 1.05 (0.88-1.25) among those with 1 to 4 years since HZ, 1.38 (1.10-1.74) for among those with 5 to 8 years since HZ, 1.28 (1.03-1.59) among those with for 9 to 12 years since HZ, and 1.19 (0.90-1.56) among those with ≥13 years since HZ. For CHD, the corresponding multivariable-adjusted hazard ratios (95% CIs) were 1.13 (1.01-1.27) for 1 to 4 years, 1.16 (1.02-1.32) for 5 to 8 years, 1.25 (1.07-1.46) for 9 to 12 years, and 1.00 (0.83-1.21) for ≥13 years. Conclusions HZ is associated with higher long-term risk of a major cardiovascular event. These findings suggest there are long-term implications of HZ and underscore the importance of prevention.

AimsCardiovascular disease is a major threat to maternal health, with cardiomyopathy being among the most common acquired cardiovascular diseases during pregnancy and the postpartum period. The aim of our study was to evaluate the effectiveness of an electrocardiogram (ECG)-based deep learning model in identifying cardiomyopathy during pregnancy and the postpartum period.Methods and resultsWe used an ECG-based deep learning model to detect cardiomyopathy in a cohort of women who were pregnant or in the postpartum period seen at Mayo Clinic. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. The study cohort included 1807 women; 7%, 10%, and 13% had left ventricular ejection fraction (LVEF) of 35% or less, <45%, and <50%, respectively. The ECG-based deep learning model identified cardiomyopathy with AUCs of 0.92 (LVEF ≤ 35%), 0.89 (LVEF < 45%), and 0.87 (LVEF < 50%). For LVEF of 35% or less, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to White (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 to 0.86 and 0.72, respectively.ConclusionsAn ECG-based deep learning model effectively identifies cardiomyopathy during pregnancy and the postpartum period and outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting.