Objective:To develop a nomogram model for predicting the risk of medication deviation in ischemic stroke patients after hospital discharge, providing insights for clinical nursing strategies.Methods:A convenience sampling method was used to select 440 ischemic stroke patients hospitalized in the Neurology Department of Nanyang Central Hospital from March 2022 to September 2023. Data on basic patient information and post-discharge medication deviation were collected using the medication deviation assessment tool eight weeks after discharge. Patients were categorized into a no medication deviation group and a medication deviation group. Univariate and binomial logistic regression analyses were conducted to identify independent risk factors for post-discharge medication deviation, and a nomogram prediction model was developed.Results:Data were successfully collected from 424 patients, with a response rate of 96.36% (424/440). The incidence of medication deviation was 58.02% (246/424). Binomial logistic regression analysis identified the absence of a fixed caregiver ( OR=2.113, P=0.001), large number of comorbid chronic diseases ( OR=1.773, P<0.001), medication non-adherence ( OR=2.443, P=0.003), and negative coping ( OR=2.255, P<0.001) as independent risk factors for medication deviation. A nomogram prediction model based on these four risk factors was constructed. The Hosmer-Lemeshow goodness-of-fit test indicated good calibration (χ 2=10.569, P=0.227), with calibration curve closely approximating the ideal line. The area under the receiver operating characteristic curve was 0.770 (95% CI: 0.584-0.866) . Conclusions:The absence of a fixed caregiver, medication non-adherence, negative coping, and large number of comorbid chronic diseases are independent risk factors for medication deviation in ischemic stroke patients. The nomogram model developed in this study demonstrates good discrimination and accuracy, providing a basis for formulating targeted nursing strategies.

Objective:To develop a nomogram model for predicting the risk of medication deviation in ischemic stroke patients after hospital discharge, providing insights for clinical nursing strategies.Methods:A convenience sampling method was used to select 440 ischemic stroke patients hospitalized in the Neurology Department of Nanyang Central Hospital from March 2022 to September 2023. Data on basic patient information and post-discharge medication deviation were collected using the medication deviation assessment tool eight weeks after discharge. Patients were categorized into a no medication deviation group and a medication deviation group. Univariate and binomial logistic regression analyses were conducted to identify independent risk factors for post-discharge medication deviation, and a nomogram prediction model was developed.Results:Data were successfully collected from 424 patients, with a response rate of 96.36% (424/440). The incidence of medication deviation was 58.02% (246/424). Binomial logistic regression analysis identified the absence of a fixed caregiver ( OR=2.113, P=0.001), large number of comorbid chronic diseases ( OR=1.773, P<0.001), medication non-adherence ( OR=2.443, P=0.003), and negative coping ( OR=2.255, P<0.001) as independent risk factors for medication deviation. A nomogram prediction model based on these four risk factors was constructed. The Hosmer-Lemeshow goodness-of-fit test indicated good calibration (χ 2=10.569, P=0.227), with calibration curve closely approximating the ideal line. The area under the receiver operating characteristic curve was 0.770 (95% CI: 0.584-0.866) . Conclusions:The absence of a fixed caregiver, medication non-adherence, negative coping, and large number of comorbid chronic diseases are independent risk factors for medication deviation in ischemic stroke patients. The nomogram model developed in this study demonstrates good discrimination and accuracy, providing a basis for formulating targeted nursing strategies.

The present paper is to verify the accuracy of DPM, a Monte Carlo-based dose calculation algorithm, in homogeneous and inhomogeneous tissues. DPM was applied to calculate (1) depth dose curves and off-axis ratios at a depth of 10 cm in water using a 6 MeV photon beam with a 3 cm x 3 cm field and phase space file simulated Varian 60 degrees C medical linear accelerator with a 10 cm x 10 cm field at SSD = 100 (cm); (2) depth dose curves using 6 MeV photon beam in inhomogeneous tissues, such as water (6 cm)/lung (6 cm)/water (8 cm) with a 3 cm x 3 cm field and water (6 cm)/ bone (2 cm)/water (12 cm) with a 10 cm x 10 cm field; (3) depth dose curves using 6 MeV photon beam based on the CT data of a patient's head and abdomen. The doses based on DPM are compared to the doses calculated by DOSXYZnrc under the same condition. The error was within 3% in water phantom while the error was within 3% in inhomogeneous tissues, except a few points. It has been concluded that the DPM can accurately predict the dose to homogeneous and inhomogeneous tissues.
Algorithms ; Computer Simulation ; Humans ; Monte Carlo Method ; Photons ; Radiation Dosage ; Radiometry ; methods ; Radiotherapy Planning, Computer-Assisted ; methods ; Software

Objective To analyze the influence of the mean energy and the full-width of half msximum(FWHM)of incident electron beam intensity distilbution(assumed Gaussian distribution)on depth dose curves and off-axis ratios and to derive a most optimal combination of mean energy and FWHM of incident electron beam intensity distribution.Methods The study simulated 6 MV photon beam produced by Varian 600C medical linear accelerator with OMEGA/EGSnrc by matching the relative error of calculated and measured depth dose curves past depth of maximum dose and off-axis ratios at a depth of 10.0 cm in water within 2%.Results The depth dose curves were relatively insensitive to the mean energy past depth of maximum dose and the FWHM of the incident electron beam intensity distribution.Dose profiles were sensitive tO the mean energy and FWHM.The dose profiles horns decreased as the mean energy and tlle FWHM of the ineident electron beam intensity distilbution increased.The calculated value of the depth dose curves matched well with the measured value.The calculated value of the off-axis ratio was consistent with the measured value within the radiation field.However, the maximum errors of individual measurement points in the penumbra region and OUt of the field reached 18.5%.Conclusions In the field.the most optimal combination of mean energy and FWHM of incident electron beam intensitv distribution Can be derived, however,can not be derived out of the field and in the penumbra region.

0.05).There were more polycystic ovary (PCO) and (or) polycystic ovarian syndrome (PCOS) patients,more basal antra] follicles,longer duration of Gn stimulation (range 16-33 days),higher Gn dose,lower serum peak estradiol (E_2) level,fewer oocytes,fewer embryos transferred,in group 1 compared with group 2 (P