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Background: Pancreatic adenocarcinoma remains a challenging malignancy with a poor prognosis. Radiofrequency ablation (RFA) has emerged as a potential treatment for unresectable pancreatic adenocarcinoma (UPAC) aimed at improving survival and quality of life. This meta-analysis and systematic review evaluates the outcomes of RFA in UPAC. Methods: A comprehensive search was conducted in MEDLINE, Embase, Scopus, and Cochrane Central databases from inception to October 2023. Studies included patients over 18 years with UAPC undergoing RFA. Survival rates and complication rates were assessed as primary outcomes. Data were pooled using random-effects models, and heterogeneity was assessed with I2 statistics. ROBINS-I tool was used for quality assessment. Results: Nine studies encompassing 265 patients met the inclusion criteria. The mean age was 64.5 years, with 42.5% female participants. Survival analysis showed that at 30 days post-RFA, the mortality rate was 3.3%. At 6 months, the mortality rate was 20.9%, increasing to 50.4% at 12 months. At 24 months, the mortality rate was 61.9%. The pooled mean survival period at 12 and 24 months was 9.18 months and 14.26 months, respectively. Overall, 78.4% of patients died during the follow-up period, with an overall mean survival period of 12.27 months. The most common were intra-abdominal (10.1%), pancreatic (9.8%), and hepatobiliary (6.7%) complications. Conclusions: RFA shows potential in the management of unresectable pancreatic adenocarcinoma, with a manageable safety profile. However, the high heterogeneity and risk of bias in available studies highlight the need for well-designed randomized controlled trials to confirm these findings and establish standardized protocols. [ABSTRACT FROM AUTHOR]

"The Wordhord: Daily Life in Old English" by Hana Videen, published in 2022, explores Old English words and names that were central to daily life in the Old English period. Videen's book focuses on words used by lay people, religious individuals, and poets between 550 CE and 1150 AD to make sense of their lives. Through an in-depth examination of proper names and common nouns, Videen provides readers with a glimpse into the cares and concerns of people during this historical period. The book contains thirteen chapters that cover various aspects of daily life, including occupations, leisure activities, health, and spirituality, offering readers a comprehensive understanding of Old English culture and language. [Extracted from the article]

Large language models (LLMs) hold promise for improving literature review of variants in clinical genetic testing. We analyzed the performance, nondeterminism, and drift of Generative Pretrained Transformer 4 (GPT-4) series models to assess their present suitability for use in complex clinical laboratory processes. We optimized a chained, two-prompt GPT-4 sequence for automated classification of functional genetic evidence in literature using a training set of 45 article-variant pairs. The initial prompt asked GPT-4 to supply all functional evidence present in a given article for the variant of interest or indicate the absence of functional evidence. For articles in which GPT-4 found functional evidence, a second prompt asked GPT-4 to classify the evidence into pathogenic, benign, or intermediate/inconclusive categories. A final test set of 72 manually classified article-variant pairs was used to test model performance over time. During a 2.5-month period from December 2023 to February 2024, we observed substantial variability in the results both within runs made in rapid succession (nondeterminism) and across days (drift). The inconsistencies lessened after January 18, 2024. Variability of results existed within and across models in the GPT-4 series, affecting various performance statistics -- for example, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) -- to different degrees. For the 20 runs starting on January 22, 2024, our initial prompt's identification of articles containing functional evidence had 92.2% sensitivity, 95.6% PPV, and 86.3% NPV. Our second prompt's pathogenic evidence detection had 90.0% sensitivity, 74.0% PPV, and 95.3% NPV, and its benign evidence detection had 88.0% sensitivity, 76.6% PPV, and 96.9% NPV. These data support the conclusion that nondeterminism and drift should be assessed and monitored for the specific metrics used to decide whether to introduce LLM functionality into clinical workflows. Failing to account for these challenges could lead to unidentified or incorrectly classified information that might be critical for patient care. The performance of our prompts appears adequate to assist in prioritization of articles but not in automated decision-making. Multiple avenues for further enhancing this performance could be explored. [ABSTRACT FROM AUTHOR]