Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Purinergic P2Y Receptor Antagonists ; Consensus ; Cardiovascular System ; Cardiology ; Asia

Methods: Relevant data from the National Readmission Database in 2015–2018 were analyzed, and the computer-assisted procedures of cervical and thoracolumbar spinal surgery were identified using International Classification of Diseases 9th and 10th revision codes. Patient demographics, surgical data, readmissions, and total charges were examined. Comorbidity burden was calculated using the Charlson and Elixhauser comorbidity index. Complication rates were determined on the basis of diagnosis codes. Results: A total of 48,116 cervical cases and 27,093 thoracolumbar cases were identified using computer-assisted navigation. No major differences in sex, age, or comorbidities over time were found. The utilization of computer-assisted navigation for cervical and thoracolumbar spinal fusion cases increased from 2015 to 2018 and normalized to their respective years’ total cases (Pearson correlation coefficient=0.756, p =0.049; Pearson correlation coefficient=0.9895, p =0.010). Total charges for cervical and thoracolumbar cases increased over time (Pearson correlation coefficient=0.758, p =0.242; Pearson correlation coefficient=0.766, p =0.234). Conclusions The use of computer-assisted navigation in spinal surgery increased significantly from 2015 to 2018. The average cost grossly increased from 2015 to 2018, and it was higher than the average cost of nonnavigated spinal surgery. With the increased utilization and standardization of computer-assisted navigation in spinal surgeries, the cost of care of more patients might potentially increase. As a result, further studies should be conducted to determine whether the use of computer-assisted navigation is efficient in terms of cost and improvement of care.

Background: Postoperative dehiscence and surgical site infection after spinal surgery can carry serious morbidity. Multidisciplinary involvement of plastic surgery is essential to minimizing morbidity and achieving definitive closure. However, a standardized approach is lacking. The aim of this study was to identify effective reconstructive interventions for the basis of an evidence-based management protocol. Methods: A retrospective review was performed at a single tertiary institution for 45 patients who required 53 reconstruction procedures with plastic surgery for wounds secondary to spinal surgery from 2010 to 2019. Statistical analysis was performed for demographics, comorbidities, and treatment methods. Primary outcomes were postoperative complications, including dehiscence, seroma, and infection. The secondary outcome was time to healing. Results: The overall complication rate was 32%, with dehiscence occurring in 17%, seroma in 15% and infection in 11% of cases. Median follow-up was 10 months (interquartile range, 4–23). Use of antibiotic beads did not affect rate of infection occurrence after wound closure (P=0.146). Use of incisional negative pressure wound therapy (iNPWT) was significant for reduced time to healing (P=0.001). Patients treated without iNPWT healed at median of 67.5 days while the patients who received iNPWT healed in 33 days. Demographics and comorbidities between these two groups were similar. Conclusions This data provides groundwork for an evidence-based approach to soft tissue reconstruction and management of dehiscence after spinal surgery. Timely involvement of plastic surgery in high-risk patients and utilization of evidence-based interventions such as iNPWT are essential for improving outcomes in this population.

There are limited data evaluating the relationship between influenza treatment and hospitalization duration. Our purpose assessed the association between different treatments and hospital stay among Korean pediatric influenza patients. Total 770 children < or = 15 yr-of-age hospitalized with community-acquired laboratory-confirmed influenza at three large urban tertiary care hospitals were identified through a retrospective medical chart review. Demographic, clinical, and cost data were extracted and a multivariable linear regression model was used to assess the associations between influenza treatment types and hospital stay. Overall, there were 81% of the patients hospitalized with laboratory-confirmed influenza who received antibiotic monotherapy whereas only 4% of the patients received oseltamivir monotherapy. The mean treatment-related charges for hospitalizations treated with antibiotics, alone or with oseltamivir, were significantly higher than those treated with oseltamivir-only (P < 0.001). Influenza patients treated with antibiotics-only and antibiotics/oseltamivir combination therapy showed 44.9% and 28.2%, respectively, longer duration of hospitalization compared to those treated with oseltamivir-only. Patients treated with antibiotics, alone or combined with oseltamivir, were associated with longer hospitalization and significantly higher medical charges, compared to patients treated with oseltamivir alone. In Korea, there is a need for more judicious use of antibiotics, appropriate use of influenza rapid testing.
Adolescent ; Anti-Bacterial Agents/*therapeutic use ; Antigens, Viral/analysis/immunology ; Antiviral Agents/*therapeutic use ; Child ; Child, Preschool ; Cohort Studies ; Demography ; Drug Therapy, Combination ; Female ; Hospitalization ; Humans ; Infant ; Infant, Newborn ; Influenza A virus/metabolism ; Influenza B virus/metabolism ; Influenza, Human/*drug therapy ; Male ; Oseltamivir/*therapeutic use ; Republic of Korea ; Retrospective Studies

BACKGROUND AND PURPOSE: This study was undertaken to determine the knowledge that people with epilepsy (PWE) have regarding the nature of epilepsy and its management, and also to identify the factors contributing to their knowledge of epilepsy. METHODS: We studied 79 consecutive PWE who visited the outpatient clinic of Seoul National University Hospital using a structured questionnaire consisting of 27 questions in 3 categories. The mean correct response rate was 61%, with 81% believing that brain cells die during a seizure, 29% considering it dangerous to take a bath or shower alone, and more than 70% believing that taking antiepileptic drugs (AEDs) will impair memory and damage the liver and kidneys. RESULTS: The mean overall correct-answer rate was significantly related to gender, length of education, type of seizures, and regularity of hospital visits (all p<0.05). CONCLUSIONS: The level of knowledge deviated significantly from the scientific data, especially in the causes of epilepsy, safety issues, and side effects of AEDs. A large-scale study should identify those PWE with the lowest knowledge of epilepsy, and then develop and implement suitable educational intervention programs to improve their knowledge.
Ambulatory Care Facilities ; Anticonvulsants ; Baths ; Brain ; Education ; Epilepsy* ; Kidney ; Liver ; Memory ; Seizures ; Seoul ; Surveys and Questionnaires