ABSTRACT
INTRODUCTION: Malignant tumors are associated with a low incidence of postoperative pancreatic fistulas. The presence of peritumoral fibrosis is considered the protective factor for the development of postoperative pancreatic fistulas after pancreatic resections for pancreatic ductal adenocarcinomas. METHODS: We analyzed a series of 109 consecutive patients with pancreatic resections for malignant pathology: pancreatic ductal adenocarcinomas and periampullary adenocarcinomas. The incidence of postoperative pancreatic fistulas has been reported in tumor histological type, in the presence of peritumoral fibrosis, and in the association between adenocarcinomas and areas of acute pancreatitis. The data obtained were processed with the statistical analysis program SPSS, and statistically significant p were considered at a value <0.05. RESULTS: For the entire study group, the incidence of postoperative pancreatic fistulas was 11.01%. The lowest incidence was observed in the group of patients with pancreatic ductal adenocarcinomas (4.06% vs. 25.72% in the group with periampullary adenocarcinoma), with a p = 0.002. The presence of peritumoral fibrous tissue was observed in 49.31% of cases without pancreatic fistulas, and in 54.54% of cases that developed this postoperative complication (p = 0.5). Also, the peritumoral fibrous tissue had a uniform distribution depending on the main diagnosis (56.14% in pancreatic ductal adenocarcinoma group vs. 37.04% in periampullary adenocarcinoma group, with a p = 0.08). In the group of patients who associated areas of acute pancreatitis on the resections, the incidence of postoperative pancreatic fistulas was 7.8 times higher (30% vs. 3.8%, p = 0.026). CONCLUSIONS: Peritumoral fibrous tissue was not a factor involved in the developing of postoperative pancreatic fistulas. The association of adenocarciomas with areas of acute pancreatitis has led to a significant increase in postoperative pancreatic fistulas, which is a significant and independent risk factor.
ABSTRACT
Preventing child abuse is a unifying goal. Making decisions that affect the lives of children is an unenviable task assigned to social services in countries around the world. The consequences of incorrectly labelling children as being at risk of abuse or missing signs that children are unsafe are well-documented. Evidence-based decision-making tools are increasingly common in social services provision but few, if any, have used social network data. We analyse a child protection services dataset that includes a network of approximately 5 million social relationships collected by social workers between 1996 and 2016 in New Zealand. We test the potential of information about family networks to improve accuracy of models used to predict the risk of child maltreatment. We simulate integration of the dataset with birth records to construct more complete family network information by including information that would be available earlier if these databases were integrated. Including family network data can improve the performance of models relative to using individual demographic data alone. The best models are those that contain the integrated birth records rather than just the recorded data. Having access to this information at the time a child's case is first notified to child protection services leads to a particularly marked improvement. Our results quantify the importance of a child's family network and show that a better understanding of risk can be achieved by linking other commonly available datasets with child protection records to provide the most up-to-date information possible.
