ABSTRACT
The SARS-CoV-2 coronavirus (COVID-19) pandemic has had widespread implications on various aspects of life; however, there is a paucity of longitudinal data regarding its impact on sexual behavior. To assess the impact of the COVID-19 pandemic on changes in frequency of sexual activity and partner dynamics in the United States, we utilized data from the General Social Survey (GSS). This biannual survey of adults in the United States, provided responses from 12,791 participants, enabling a comparative analysis of sexual behaviors between pre-COVID-19 (2016 and 2018, n = 5215 respondents) and COVID-19 periods (2021 and 2022, n = 7576 respondents). Sexual behavior was evaluated using variables "sexfreq" (frequency of sexual encounters) and 'partners' (number of sexual partners). Overall, the percentage of respondents reporting more than one sexual partner in the last year decreased from 13.8% pre-pandemic to 9.8% during the pandemic (p = 0.002). The percentage of participants who reported engaging in sexual activity at least monthly decreased from 63.9% pre-pandemic to 58.9% during the pandemic (p = 0.02), and those reporting at least weekly sexual activity also decreased from 35.8% to 30.6% (p = 0.001). On sub-group analysis, men showed no change in the frequency of sexual activity compared to pre-pandemic levels; however, men engaged with fewer partners, with a decrease from 18.6% reporting more than one sexual partner to 11.7% (p = 0.001). Among women, specifically non-married women, there was a significant decrease in frequency of sexual encounters-from 49.2% to 40.5% for sex at least once a month (p = 0.006) and from 29.2% to 21.1% for sex at least once a week (p = 0.001). Non-married women also showed a decrease in the number of sexual partners from 58.7% to 50.6% (p = 0.01) during the COVID-19 pandemic. The COVID-19 pandemic significantly changed sexual behaviors and partner choices, leading to fewer sexual partners and less frequent sexual activity, particularly in non-married women.
ABSTRACT
BACKGROUND: Artificial intelligence (AI) is engineered to emulate tasks that have historically required human interaction and intellect, including learning, pattern recognition, decision-making, and problem-solving. Although AI models like ChatGPT-4 have demonstrated satisfactory performance on medical licensing exams, suggesting a potential for supporting medical diagnostics and decision-making, no study of which we are aware has evaluated the ability of these tools to make treatment recommendations when given clinical vignettes and representative medical imaging of common orthopaedic conditions. As AI continues to advance, a thorough understanding of its strengths and limitations is necessary to inform safe and helpful integration into medical practice. QUESTIONS/PURPOSES: (1) What is the concordance between ChatGPT-4-generated treatment recommendations for common orthopaedic conditions with both the American Academy of Orthopaedic Surgeons (AAOS) clinical practice guidelines (CPGs) and an orthopaedic attending physician's treatment plan? (2) In what specific areas do the ChatGPT-4-generated treatment recommendations diverge from the AAOS CPGs? METHODS: Ten common orthopaedic conditions with associated AAOS CPGs were identified: carpal tunnel syndrome, distal radius fracture, glenohumeral joint osteoarthritis, rotator cuff injury, clavicle fracture, hip fracture, hip osteoarthritis, knee osteoarthritis, ACL injury, and acute Achilles rupture. For each condition, the medical records of 10 deidentified patients managed at our facility were used to construct clinical vignettes that each had an isolated, single diagnosis with adequate clarity. The vignettes also encompassed a range of diagnostic severity to evaluate more thoroughly adherence to the treatment guidelines outlined by the AAOS. These clinical vignettes were presented alongside representative radiographic imaging. The model was prompted to provide a single treatment plan recommendation. Each treatment plan was compared with established AAOS CPGs and to the treatment plan documented by the attending orthopaedic surgeon treating the specific patient. Vignettes where ChatGPT-4 recommendations diverged from CPGs were reviewed to identify patterns of error and summarized. RESULTS: ChatGPT-4 provided treatment recommendations in accordance with the AAOS CPGs in 90% (90 of 100) of clinical vignettes. Concordance between ChatGPT-generated plans and the plan recommended by the treating orthopaedic attending physician was 78% (78 of 100). One hundred percent (30 of 30) of ChatGPT-4 recommendations for fracture vignettes and hip and knee arthritis vignettes matched with CPG recommendations, whereas the model struggled most with recommendations for carpal tunnel syndrome (3 of 10 instances demonstrated discordance). ChatGPT-4 recommendations diverged from AAOS CPGs for three carpal tunnel syndrome vignettes; two ACL injury, rotator cuff injury, and glenohumeral joint osteoarthritis vignettes; as well as one acute Achilles rupture vignette. In these situations, ChatGPT-4 most often struggled to correctly interpret injury severity and progression, incorporate patient factors (such as lifestyle or comorbidities) into decision-making, and recognize a contraindication to surgery. CONCLUSION: ChatGPT-4 can generate accurate treatment plans aligned with CPGs but can also make mistakes when it is required to integrate multiple patient factors into decision-making and understand disease severity and progression. Physicians must critically assess the full clinical picture when using AI tools to support their decision-making. CLINICAL RELEVANCE: ChatGPT-4 may be used as an on-demand diagnostic companion, but patient-centered decision-making should continue to remain in the hands of the physician.
ABSTRACT
When feasible from an oncologic standpoint, partial penectomy (PP) is often preferred to total penectomy (TP) for penile cancer treatment, for the preservation of functional urinary outcomes. However, to date, there has not been a direct comparison of perioperative outcomes between PP and TP. Comparing treatments for penile cancer has proven difficult due to the rarity of penile cancer in the United States. We aimed to report differences in pre-operative risk factors, intra-operative outcomes, and postoperative outcomes between TP and PP for penile cancer. Using the National Surgical Quality Improvement Program database, we conducted a retrospective cohort review of penile cancer patients enlisted in the database between the years 2006-2016 using the International Classification of Diseases clinical modification 9th revision codes. A total of 260 patients, 67 TP and 193 PP patients, were included. PP patients were less likely to be transferred patients (p = 0.002), diabetic (p = 0.026), and were more likely to have preoperative laboratory values within normal limits. PP patients also had shorter lengths of stay in the hospital (p < 0.001) and operating time (p < 0.001). Significant differences were also found for inpatient stay (p < 0.001), 30-day post-surgery complications (p < 0.001), deep incisional surgical site infection (SSI) (p = 0.017), wound disruption (p = 0.017), intraoperative or postoperative transfusion (p = 0.029), and sepsis (p < 0.005). Finally, PP patients required fewer concurrent surgical procedures (p < 0.001). Demographic differences between PP and TP patients may reflect patients presenting with more advanced oncologic disease. PP is associated with fewer postoperative complications, shorter surgeries, shorter hospital stays, fewer concurrent surgical procedures, and comorbid conditions compared to TP. A gap remains in the reported data pertaining to postoperative sexual function and erectile outcomes for PP at a national level.
ABSTRACT
INTRODUCTION: Clinical documentation is a critical aspect of health care that enables healthcare providers to communicate effectively with each other and maintain accurate patient care records. Artificial intelligence tools, such as chatbots and virtual assistants, have the potential to assist healthcare providers in clinical documentation. ChatGPT is an artificial intelligence conversational model that generates human-like responses to text-based prompts. In this study, we sought to investigate ChatGPT's ability to assist with writing a history of present illness based on standardized patient histories. METHODS: A blinded, randomized controlled study was conducted to compare the use of typing, dictation, and ChatGPT as tools to document history of present illness (HPI) of standardized patient histories. Eleven study participants, consisting of medical students, orthopaedic surgery residents, and attending surgeons, completed three HPIs using a different documentation technique for each one. Participants were randomized into cohorts based on the type of documentation technique. Participants were asked to interview standardized patients and document the patient's history of present illness using their assigned method. RESULTS: ChatGPT was found to be intermediate for speed; dictation was fastest, but produced markedly longer and higher quality patient histories based on Physician Documentation Quality Instrument score compared with dictation and typing. However, ChatGPT included erroneous information in 36% of the documents. Poor agreement existed on the quality of patient histories between reviewers. DISCUSSION: Our study suggests that ChatGPT has the potential to improve clinical documentation by producing more comprehensive and organized HPIs. ChatGPT can generate longer and more detailed documentation compared with typing or dictation documentation methods. However, additional studies are needed to investigate and address concerns regarding privacy, bias, and accuracy of information.