More and more researchers are becoming involved through a variety of actions and programs, which divides assessment into different branches(such as formative, summative, normative assessment, etc). In the present pape...More and more researchers are becoming involved through a variety of actions and programs, which divides assessment into different branches(such as formative, summative, normative assessment, etc). In the present paper, I will try to emphasis that collaborative assessment is positive to high school students' learning outcome, and cooperative and competitive assessment should be mixture implemented in Chinese education process rather than only using competitive assessment to evaluate students' achievement. I hope that Chinese students and teachers could teach or test in the less pressure by reforming the educational evaluation system.展开更多
Some students make negative comments on their learning outcomes after two years＇ college English learning. The article is to investigate factors influencing students＇ evaluation of their learning, including students...Some students make negative comments on their learning outcomes after two years＇ college English learning. The article is to investigate factors influencing students＇ evaluation of their learning, including students＇ self-efficacy, learning strategies and different categories of achievement goals.展开更多
To estimate the short-term results of robot-assisted laparoscopic radical prostatectomy(RALRP)during the learning curve,in terms of surgical,oncological and functional outcomes,we conducted a prospective survey on RAL...To estimate the short-term results of robot-assisted laparoscopic radical prostatectomy(RALRP)during the learning curve,in terms of surgical,oncological and functional outcomes,we conducted a prospective survey on RALRP.From July 2007,a single surgeon performed 63 robotic prostatectomies using the same operative technique.Perioperative data,including pathological and early functional results of the patient,were collected prospectively and analyzed.Along with the accumulation of the cases,the total operative time,setup time,console time and blood loss were significantly decreased.No major complication was present in any patient.Transfusion was needed in six patients;all of them were within the initial 15 cases.The positive surgical margin rate was 9.8%(5/51)in pT2 disease.The most frequent location of positive margin in this stage was the lateral aspect(60%),but in pT3 disease multiple margins were the most frequent(41.7%).Overall,53(84.1%)patients had totally continent status and the median time to continence was 6.56 weeks.Among 17 patients who maintained preoperative sexual activity(Sexual Health Inventory for Men≥17),stage below pT2,followed up for>6 months with minimally one side of neurovascular bundle preservation procedure,12(70.6%)were capable of intercourse postoperatively,and the mean time for sexual intercourse after operation was 5.7 months.In this series,robotic prostatectomy was a feasible and reproducible technique,with a short learning curve and low perioperative complication rate.Even during the initial phase of the learning curve,satisfactory results were obtained with regard to functional and oncological outcome.展开更多
BACKGROUND Intensive care unit(ICU)patients demand continuous monitoring of several clinical and laboratory parameters that directly influence their medical progress and the staff’s decision-making.Those data are vit...BACKGROUND Intensive care unit(ICU)patients demand continuous monitoring of several clinical and laboratory parameters that directly influence their medical progress and the staff’s decision-making.Those data are vital in the assistance of these patients,being already used by several scoring systems.In this context,machine learning approaches have been used for medical predictions based on clinical data,which includes patient outcomes.AIM To develop a binary classifier for the outcome of death in ICU patients based on clinical and laboratory parameters,a set formed by 1087 instances and 50 variables from ICU patients admitted to the emergency department was obtained in the“WiDS(Women in Data Science)Datathon 2020:ICU Mortality Prediction”dataset.METHODS For categorical variables,frequencies and risk ratios were calculated.Numerical variables were computed as means and standard deviations and Mann-Whitney U tests were performed.We then divided the data into a training(80%)and test(20%)set.The training set was used to train a predictive model based on the Random Forest algorithm and the test set was used to evaluate the predictive effectiveness of the model.RESULTS A statistically significant association was identified between need for intubation,as well predominant systemic cardiovascular involvement,and hospital death.A number of the numerical variables analyzed(for instance Glasgow Coma Score punctuations,mean arterial pressure,temperature,pH,and lactate,creatinine,albumin and bilirubin values)were also significantly associated with death outcome.The proposed binary Random Forest classifier obtained on the test set(n=218)had an accuracy of 80.28%,sensitivity of 81.82%,specificity of 79.43%,positive predictive value of 73.26%,negative predictive value of 84.85%,F1 score of 0.74,and area under the curve score of 0.85.The predictive variables of the greatest importance were the maximum and minimum lactate values,adding up to a predictive importance of 15.54%.CONCLUSION We demonstrated the efficacy of a Random Forest machine learning algorithm for handling clinical and laboratory data from patients under intensive monitoring.Therefore,we endorse the emerging notion that machine learning has great potential to provide us support to critically question existing methodologies,allowing improvements that reduce mortality.展开更多
文摘More and more researchers are becoming involved through a variety of actions and programs, which divides assessment into different branches(such as formative, summative, normative assessment, etc). In the present paper, I will try to emphasis that collaborative assessment is positive to high school students' learning outcome, and cooperative and competitive assessment should be mixture implemented in Chinese education process rather than only using competitive assessment to evaluate students' achievement. I hope that Chinese students and teachers could teach or test in the less pressure by reforming the educational evaluation system.
文摘Some students make negative comments on their learning outcomes after two years＇ college English learning. The article is to investigate factors influencing students＇ evaluation of their learning, including students＇ self-efficacy, learning strategies and different categories of achievement goals.
基金This study was carried out without any commercial sponsorship from equipment manufacturers.
文摘To estimate the short-term results of robot-assisted laparoscopic radical prostatectomy(RALRP)during the learning curve,in terms of surgical,oncological and functional outcomes,we conducted a prospective survey on RALRP.From July 2007,a single surgeon performed 63 robotic prostatectomies using the same operative technique.Perioperative data,including pathological and early functional results of the patient,were collected prospectively and analyzed.Along with the accumulation of the cases,the total operative time,setup time,console time and blood loss were significantly decreased.No major complication was present in any patient.Transfusion was needed in six patients;all of them were within the initial 15 cases.The positive surgical margin rate was 9.8%(5/51)in pT2 disease.The most frequent location of positive margin in this stage was the lateral aspect(60%),but in pT3 disease multiple margins were the most frequent(41.7%).Overall,53(84.1%)patients had totally continent status and the median time to continence was 6.56 weeks.Among 17 patients who maintained preoperative sexual activity(Sexual Health Inventory for Men≥17),stage below pT2,followed up for>6 months with minimally one side of neurovascular bundle preservation procedure,12(70.6%)were capable of intercourse postoperatively,and the mean time for sexual intercourse after operation was 5.7 months.In this series,robotic prostatectomy was a feasible and reproducible technique,with a short learning curve and low perioperative complication rate.Even during the initial phase of the learning curve,satisfactory results were obtained with regard to functional and oncological outcome.
文摘BACKGROUND Intensive care unit(ICU)patients demand continuous monitoring of several clinical and laboratory parameters that directly influence their medical progress and the staff’s decision-making.Those data are vital in the assistance of these patients,being already used by several scoring systems.In this context,machine learning approaches have been used for medical predictions based on clinical data,which includes patient outcomes.AIM To develop a binary classifier for the outcome of death in ICU patients based on clinical and laboratory parameters,a set formed by 1087 instances and 50 variables from ICU patients admitted to the emergency department was obtained in the“WiDS(Women in Data Science)Datathon 2020:ICU Mortality Prediction”dataset.METHODS For categorical variables,frequencies and risk ratios were calculated.Numerical variables were computed as means and standard deviations and Mann-Whitney U tests were performed.We then divided the data into a training(80%)and test(20%)set.The training set was used to train a predictive model based on the Random Forest algorithm and the test set was used to evaluate the predictive effectiveness of the model.RESULTS A statistically significant association was identified between need for intubation,as well predominant systemic cardiovascular involvement,and hospital death.A number of the numerical variables analyzed(for instance Glasgow Coma Score punctuations,mean arterial pressure,temperature,pH,and lactate,creatinine,albumin and bilirubin values)were also significantly associated with death outcome.The proposed binary Random Forest classifier obtained on the test set(n=218)had an accuracy of 80.28%,sensitivity of 81.82%,specificity of 79.43%,positive predictive value of 73.26%,negative predictive value of 84.85%,F1 score of 0.74,and area under the curve score of 0.85.The predictive variables of the greatest importance were the maximum and minimum lactate values,adding up to a predictive importance of 15.54%.CONCLUSION We demonstrated the efficacy of a Random Forest machine learning algorithm for handling clinical and laboratory data from patients under intensive monitoring.Therefore,we endorse the emerging notion that machine learning has great potential to provide us support to critically question existing methodologies,allowing improvements that reduce mortality.
