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Objectives: In this study we wished to determine the rank order of risk factors for endometrial cancer and calculate a pooled risk and percentage risk for each factor using a statistical meta-analysis approach. The next step was to design a neural network computer model to predict the overall increase or decreased risk of cancer for individual patients. This would help to determine whether this prediction could be used as a tool to decide if a patient should be considered for testing and to predict diagnosis, as well as to suggest prevention measures to patients. Design: A meta-analysis of existing data was carried out to calculate relative risk, followed by design and implementation of a risk prediction computational model based on a neural network algorithm. Setting: Meta-analysis data were collated from various settings from around the world. Primary data to test the model were collected from a hospital clinic setting. Participants: Data from 40 patients notes currently suspected of having endometrial cancer and undergoing investigations and treatment were collected to test the software with their cancer diagnosis not revealed to the software developers. Main outcome measures: The forest plots allowed an overall relative risk and percentage risk to be calculated from all the risk data gathered from the studies. A neural network computational model to determine percentage risk for individual patients was developed, implemented, and evaluated. Results: The results show that the greatest percentage increased risk was due to BMI being above 25, with the risk increasing as BMI increases. A BMI of 25 or over gave an increased risk of 2.01%, a BMI of 30 or over gave an increase of 5.24%, and a BMI of 40 or over led to an increase of 6.9%. PCOS was the second highest increased risk at 4.2%. Diabetes, which is incidentally also linked to an increased BMI, gave a significant increased risk along with null parity and noncontinuous HRT of 1.54%, 1.2%, and 0.56% respectively. Decreased risk due to contraception was greatest with IUD (intrauterine device) and IUPD (intrauterine progesterone device) at −1.34% compared to −0.9% with oral. Continuous HRT at −0.75% and parity at −0.9% also decreased the risk. Using open-source patient data to test our computational model to determine risk, our results showed that the model is 98.6% accurate with an algorithm sensitivity 75% on average. Conclusions: In this study, we successfully determined the rank order of risk factors for endometrial cancer and calculated a pooled risk and risk percentage for each factor using a statistical meta-analysis approach. Then, using a computer neural network model system, we were able to model the overall increase or decreased risk of cancer and predict the cancer diagnosis for particular patients to an accuracy of over 98%. The neural network model developed in this study was shown to be a potentially useful tool in determining the percentage risk and predicting the possibility of a given patient developing endometrial cancer. As such, it could be a useful tool for clinicians to use in conjunction with other biomarkers in determining which patients warrant further preventative interventions to avert progressing to endometrial cancer. This result would allow for a reduction in the number of unnecessary invasive tests on patients. The model may also be used to suggest interventions to decrease the risk for a particular patient. The sensitivity of the model limits it at this stage due to the small percentage of positive cases in the datasets; however, since this model utilizes a neural network machine learning algorithm, it can be further improved by providing the system with more and larger datasets to allow further refinement of the neural network.

Research shows that anxiety can disrupt learning processes, but few studies have examined anxiety’s relationships to online learning behaviors. This study considers the interplay between students’ anxiety about science and behavior within an online system designed to support self-regulated science inquiry. Using the searching, monitoring, assessing, rehearsing, and translating (SMART) classification schema for self-regulated learning (SRL), we leverage microanalysis of self-regulated behaviors to better understand how science anxiety inhibits (or supports) different learning operations. Specifically, we show that while science anxiety is positively associated with searching behaviors, it is negatively associated with monitoring behaviors, suggesting that anxious students may avoid evaluation, opting instead to compensate with information-seeking. These findings help us to better understand SRL processes and may also help us support anxious students in developing SRL strategies.

Discusses the vital importance of the preschool period in educational terms and maintains that early influences are much more effective than later experience; one cannot look upon nursery schools as merely child care services. (MS)