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Abstract Background We aimed to assess the trends in urinary tract infections (UTIs) and prognosis of patients with prostate cancer after radical prostatectomy (RP) and radiation therapy (RT) as definitive treatment options. Methods The data of patients diagnosed with prostate cancer between 2007 and 2016 were collected from the National Health Insurance Service database. The incidence of UTIs was evaluated in patients treated with RT, open/laparoscopic RP, and robot-assisted RP. The proportional hazard assumption test was performed using the scaled Schoenfeld residuals based on a multivariable Cox proportional hazard model. Kaplan–Meier analysis were performed to assess survival. Results A total of 28,887 patients were treated with definitive treatment. In the acute phase ( 12 months), UTIs were more frequent in RT than in RP. In the early follow-up period, the risk of UTIs was higher in the open/laparoscopic RP group (aHR, 1.63; 95% CI, 1.44–1.83; p

Estimating the measurement uncertainty (MU) is becoming increasingly mandatory in analytical toxicology. This study evaluates the uncertainty in the quantitative determination of urinary amphetamine (AP) and 4-hydroxyamphetamine (4HA) using a liquid chromatography–tandem mass spectrometry (LC–MS/MS) method based on the dilute-and-shoot approach. Urine sample dilution, preparation of calibrators, calibration curve, and method repeatability were identified as the sources of uncertainty. To evaluate the MU, the Guide to the Expression of Uncertainty in Measurement (GUM) approach and the Monte Carlo method (MCM) were compared using the R programming language. The MCM afforded a smaller coverage interval for both AP (94.83, 104.74) and 4HA (10.52, 12.14) than that produced by the GUM (AP (92.06, 107.41) and 4HA (10.21, 12.45)). The GUM approach offers an underestimated coverage interval for Type A evaluation, whereas the MCM provides an exact coverage interval under an abnormal probability distribution of the measurand. The MCM is useful in complex settings where the measurand is combined with numerous distributions because it is generated from the uncertainties of input quantities based on the propagation of the distribution. Therefore, the MCM is more practical than the GUM for evaluating the MU of urinary AP and 4HA concentrations using LC–MS/MS.