Lindgaardmartensen2041

is cost-effective compared to other risk-based strategies. However, applying a 20% threshold for probable LNI to the Briganti 2012 nomogram or the Memorial Sloan Kettering Cancer Center web-calculator, may be a feasible alternative, in particular when higher WTP values are considered.

Poorly controlled pain in patients is related to several complications, such as increased nosocomial infections and mortality, where nurses play a crucial role.

To analyze determinants of pain as well as nurses' knowledge and attitudes towards pain in the inpatient services of a tertiary center in the Spanish public health network.

The Knowledge and Attitudes Survey Regarding Pain questionnaire was administered to all nurses in the center from January to March 2019. Compound 3 in vitro Additional sociodemographic variables, such as gender, age, employment status, work experience, professional group, and academic degree, were collected and analyzed. Item Response Theory was used for discriminant analysis of each question and its relationship with the final score.

A total of 282 questionnaires were collected from those distributed among nurses working in medical, surgical, oncological, and intensive care services. The average score obtained on pain-related knowledge and attitudes was 58.89%. We found significant differences (p<.001) between the KASRP score and the professional group score. There were no differences in final score based on academic level or age. Questions related to pharmacology resulted in low scores and did not discriminate between levels of knowledge, being considered difficult. We did not find items that allowed discriminating between levels of knowledge.

A knowledge gap exists regarding nurses' pharmacological and assessment concepts, and there are differences in knowledge depending on professional group. The KASRP allows for a good discrimination of low levels of knowledge.

A knowledge gap exists regarding nurses' pharmacological and assessment concepts, and there are differences in knowledge depending on professional group. The KASRP allows for a good discrimination of low levels of knowledge.The application of machine learning and deep learning in the field of imaging is rapidly growing. Although the principles of machine and deep learning are unfamiliar to the majority of clinicians, the basics are not so complicated. One of the major issues is that commentaries written by experts are difficult to understand, and are not primarily written for clinicians. The purpose of this article was to describe the different concepts behind machine learning, radiomics, and deep learning to make clinicians more familiar with these techniques.

Place of death is important to patients and caregivers, and often a surrogate measure of health care disparities. While recent trends in place of death suggest an increased frequency of dying at home, data is largely unknown for older adults with cancer.

Deidentified death certificate data were obtained via the National Center for Health Statistics. All lung, colon, prostate, breast, and pancreas cancer deaths for older adults (defined as >65years of age) from 2003 to 2017 were included. Multinomial logistic regression was used to test for differences in place of death associated with sociodemographic variables.

From 2003 through 2017, a total of 3,182,707 older adults died from lung, colon, breast, prostate and pancreas cancer. During this time, hospital and nursing home deaths decreased, and the rate of home and hospice facility deaths increased (all p<0.001). In multivariable regression, all assessed variables were found to be associated with place of death. Overall, older age was associated with increased risk of nursing facility death versus home death. Black patients were more likely to experience hospital death (OR 1.7) and Hispanic ethnicity had lower odds of death in a nursing facility (OR 0.55). Since 2003, deaths in hospice facilities rapidly increased by 15%.

Hospital and nursing facility cancer deaths among older adults with cancer decreased since 2003, while deaths at home and hospice facilities increased. Differences in place of death were noted for non-white patients and older adults of advanced age.

Hospital and nursing facility cancer deaths among older adults with cancer decreased since 2003, while deaths at home and hospice facilities increased. Differences in place of death were noted for non-white patients and older adults of advanced age.

This study aimed to investigate the preoperative monocyte-to-lymphocyte ratio (MLR) as a biomarker for intravesical recurrence (IVR) in upper urinary tract urothelial carcinoma (UTUC) after radical nephroureterectomy (RNU) for the first time.

This study involved the clinical data of 255 patients with UTUC without a history of bladder cancer who had undergone RNU from March 2004 to February 2019 at an academic institution. The associations between MLR and IVR were assessed with Kaplan-Meier method and Cox regression analysis.

The median follow-up was 43.93 months. Of the 255 patients, 37 developed IVR during the follow-up period. Kaplan-Meier analysis revealed that patients with high MLR (> 0.22) had poor IVR-free survival (P= .001); this prognostic value was in accordance with patients with high grade and more advanced stage UTUC. Cox regression preoperative models showed that ureteral tumor site (hazard ratio [HR], 2.784; P= .005), surgical approach (HR, 2.745; P= .008), and high MLR (HR, 4.085; P< .001) were an independent risk factor for IVR. These factors were used as a signature to establish a prognostic risk model, which revealed significant differences among the 3 subgroups of patients with low, intermediate, and high risk (P< .001).

Ureteral tumor site, surgical approach, and preoperative MLR are significant predictors for IVR in patients with UTUC after RNU. MLR may become a useful biomarker to predict IVR in patients with UTUC after RNU.

Ureteral tumor site, surgical approach, and preoperative MLR are significant predictors for IVR in patients with UTUC after RNU. MLR may become a useful biomarker to predict IVR in patients with UTUC after RNU.