Methodological insights into ChatGPT's screening performance in systematic reviews.

BACKGROUND: The screening process for systematic reviews and meta-analyses in medical research is a labor-intensive and time-consuming task. While machine learning and deep learning have been applied to facilitate this process, these methods often require training data and user annotation. This study aims to assess the efficacy of ChatGPT, a large language model based on the Generative Pretrained Transformers (GPT) architecture, in automating the screening process for systematic reviews in radiology without the need for training data. METHODS: A prospective simulation study was conducted between May 2nd and 24th, 2023, comparing ChatGPT's performance in screening abstracts against that of general physicians (GPs). A total of 1198 abstracts across three subfields of radiology were evaluated. Metrics such as sensitivity, specificity, positive and negative predictive values (PPV and NPV), workload saving, and others were employed. Statistical analyses included the Kappa coefficient for inter-rater agreement, ROC curve plotting, AUC calculation, and bootstrapping for p-values and confidence intervals. RESULTS: ChatGPT completed the screening process within an hour, while GPs took an average of 7-10 days. The AI model achieved a sensitivity of 95% and an NPV of 99%, slightly outperforming the GPs' sensitive consensus (i.e., including records if at least one person includes them). It also exhibited remarkably low false negative counts and high workload savings, ranging from 40 to 83%. However, ChatGPT had lower specificity and PPV compared to human raters. The average Kappa agreement between ChatGPT and other raters was 0.27. CONCLUSIONS: ChatGPT shows promise in automating the article screening phase of systematic reviews, achieving high sensitivity and workload savings. While not entirely replacing human expertise, it could serve as an efficient first-line screening tool, particularly in reducing the burden on human resources. Further studies are needed to fine-tune its capabilities and validate its utility across different medical subfields.

Efficacy and Safety of Preoperative Embolization in Glomus Jugulare Tumors: A Systematic Review and Meta-analysis of Clinical Outcomes and Complications.

PURPOSE: This study aims to provide a comprehensive review of the clinical benefits, complications, and safety profile associated with preoperative embolization in Glomus jugulare tumors (GJTs). MATERIALS AND METHODS: A comprehensive search in PubMed, Embase, and Web of Science was conducted for English articles published up to March 2023, focusing on GJTs and preoperative embolization. Included studies involved patients over 18 with GJTs. We excluded studies that explored embolization methods other than the standard endovascular approach, as well as studies involving paragangliomas that did not provide specific data related to GJTs. Key variables such as hemorrhage volume and surgical time, as well as clinical outcomes, were analyzed. Data were analyzed using a random-effects model meta-analysis, assessing heterogeneity with the I2 statistic. RESULTS: This review encompasses 19 studies with a total of 328 patients. The studies incorporated into our meta-analysis display considerable differences and inconsistencies in their data. The findings of the meta-analysis show a mean hemorrhage volume of 636 ml (95% confidence interval (CI) 473-799) following preoperative embolization, and a mean surgical duration of 487 min (95% CI 350-624). The study also notes potential complications: facial nerve deficits occurred in 20% of cases (95% CI 11-32%), and vagal nerve deficits in 22% (95% CI 13-31%). CONCLUSION: This study suggests that preoperative embolization could decrease surgery duration and blood loss, but emphasizes the importance of evaluating risks like nerve damage. However, the generalizability of these findings is restricted due to the diversity of available data.

Risk scores for prediction of paroxysmal atrial fibrillation after acute ischemic stroke or transient ischemic attack: A systematic review and meta-analysis.

Introduction: Detection of paroxysmal atrial fibrillation (PAF) is crucial for secondary prevention in patients with recent strokes of unknown etiology. This systematic review and meta-analysis assess the predictive power of available risk scores for detecting new PAF after acute ischemic stroke (AIS). Methods: PubMed, Embase, Scopus, and Web of Science databases were searched until September 2023 to identify relevant studies. A bivariate random effects meta-analysis model pooled data on sensitivity, specificity, and area under the curve (AUC) for each score. The QUADAS-2 tool was used for the quality assessment. Results: Eventually, 21 studies with 18 original risk scores were identified. Age, left atrial enlargement, and NIHSS score were the most common predictive factors, respectively. Seven risk scores were meta-analyzed, with iPAB showing the highest pooled sensitivity and AUC (sensitivity: 89.4%, specificity: 74.2%, AUC: 0.83), and HAVOC having the highest pooled specificity (sensitivity: 46.3%, specificity: 82.0%, AUC: 0.82). Altogether, seven risk scores displayed good discriminatory power (AUC ≥0.80) with four of them (HAVOC, iPAB, Fujii, and MVP scores) being externally validated. Conclusion: Available risk scores demonstrate moderate to good predictive accuracy and can help identify patients who would benefit from extended cardiac monitoring after AIS. External validation is essential before widespread clinical adoption.

Concurrent adrenal and extra-adrenal myelolipoma: A case report.

INTRODUCTION: Myelolipoma, a benign tumor characterized by mature fat cells and hematopoietic cells, is predominantly found in the adrenal glands, accounting for 6-16 % of all adrenal tumors. These tumors are often asymptomatic and discovered incidentally during imaging. We present a rare case of concurrent adrenal and extra-adrenal myelolipomas, contributing to the limited research in this area. CASE PRESENTATION: A 65-year-old female with a history of Steven-Johnson syndrome presented with epigastric pain, initially diagnosed with emphysematous cholecystitis. Imaging revealed unexpected lesions near the left kidney. During surgery for presumed cholecystitis, significant hemorrhaging occurred following an attempted biopsy of the left adrenal lesion. This complication necessitated a complete adrenalectomy. Pathological examination confirmed the presence of myelolipomas in the left adrenal gland, para-aortic, and left para-iliac regions. DISCUSSION: The simultaneous occurrence of adrenal and extra-adrenal myelolipomas is exceptionally rare, posing diagnostic and management challenges. This case highlights the complexity of managing patients with multiple comorbidities and the critical importance of differentiating myelolipomas from other fat-containing retroperitoneal masses. The incidental discovery of these tumors and their potential for significant intraoperative complications, as seen in our case, underscores the need for careful surgical planning and thorough preoperative assessment. CONCLUSION: This case emphasizes the diagnostic challenges and management complexities in patients with incidental findings of myelolipoma, particularly when accompanied by significant medical histories. The occurrence of unexpected intraoperative complications highlights the importance of cautious decision-making in surgical interventions. This report provides valuable insights into the unpredictable nature of medical practice and the management of rare pathologies.

Long-Term Clinical and Imaging Findings in Patients with Lower Extremity Varicose Veins Treated with Endovenous Laser Treatment: A Follow-Up Study of up to 12 Years.

Introduction: This study investigates the long-term effectiveness and safety of endovenous laser treatment (EVLT) for chronic venous insufficiency (CVI), a condition commonly caused by dysfunctional valves in the venous circulation system. Materials and Methods: In this retrospective cohort study, patients underwent EVLT and were followed up for successive short intervals and one last time after a median duration of 9-year postprocedural. Pre- and postprocedure duplex ultrasound was used to assess changes in the great saphenous vein (GSV) diameter, reflux, and saphenofemoral junction incompetence. Quality of life was evaluated using the SF-36 and Aberdeen Varicose Vein Questionnaire (AVVQ). Results: Sixty-eight patients with a mean age of 52.4 ± 12.4 years were enrolled in the study. The mean follow-up time was 8.9 ± 2.1 years, ranging from 5 to 12 years. The mean GSV diameter significantly decreased in all patients (whole group) across proximal (from 5.8 ± 2.3 mm to 4.2 ± 2.1 mm), middle (from 4.7 ± 1.6 mm to 2.8 ± 2.2 mm), and distal (from 4.5 ± 2.3 mm to 2.2 ± 2.2 mm) segments, with P < 0.001. A disease recurrence rate of 33.8% was noted, predominantly in male patients and those with larger middle GSV diameters (OR = 5.2 (95%CI = 1.3-20.4) and OR = 1.5 (95%CI = 1-2.1), respectively). The average follow-up time for patients without recurrence was 8.8 ± 2.1 years. Almost half of the patients without recurrence were followed up for 10 years or more (49%). Conclusion: The efficacy of EVLT in managing varicose veins is demonstrated by its relatively low recurrence rate over a 10-year follow-up period, highlighting EVLT as a viable long-term treatment strategy.

Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models.

BACKGROUND: To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes. MAIN BODY: A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics. RESULTS: In total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues. CONCLUSION: AI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy.

Subtyping irritable bowel syndrome using cluster analysis: a systematic review.

BACKGROUND: Irritable bowel syndrome (IBS) is a common chronic functional gastrointestinal disorder associated with a wide range of clinical symptoms. Some researchers have used cluster analysis (CA), a group of non-supervised learning methods that identifies homogenous clusters within different entities based on their similarity. OBJECTIVE AND METHODS: This literature review aims to identify published articles that apply CA to IBS patients. We searched relevant keywords in PubMed, Embase, Web of Science, and Scopus. We reviewed studies in terms of the selected variables, participants' characteristics, data collection, methodology, number of clusters, clusters' profiles, and results. RESULTS: Among the 14 articles focused on the heterogeneity of IBS, eight of them utilized K-means Cluster Analysis (K-means CA), four employed Hierarchical Cluster Analysis, and only two studies utilized Latent Class Analysis. Seven studies focused on clinical symptoms, while four articles examined anocolorectal functions. Two studies were centered around immunological findings, and only one study explored microbial composition. The number of clusters obtained ranged from two to seven, showing variation across the studies. Males exhibited lower symptom severity and fewer psychological findings. The association between symptom severity and rectal perception suggests that altered rectal perception serves as a biological indicator of IBS. Ultra-slow waves observed in IBS patients are linked to increased activity of the anal sphincter, higher anal pressure, dystonia, and dyschezia. CONCLUSION: IBS has different subgroups based on different factors. Most IBS patients have low clinical severity, good QoL, high rectal sensitivity, delayed left colon transit time, increased systemic cytokines, and changes in microbial composition, including increased Firmicutes-associated taxa and depleted Bacteroidetes-related taxa. However, the number of clusters is inconsistent across studies due to the methodological heterogeneity. CA, a valuable non-supervised learning method, is sensitive to hyperparameters like the number of clusters and random initialization of cluster centers. The random nature of these parameters leads to diverse outcomes even with the same algorithm. This has implications for future research and practical applications, necessitating further studies to improve our understanding of IBS and develop personalized treatments.