With clinical medicine science transforming from traditional medicine to evidence-based medicine, how to practice evidence-based medicine has become a new challenge to clinical doctors. Therapy studies play an important part in clinical studies and how to practice evidence-based medicine in the therapy of diseases is an important question that doctors are concerned. This paper will introduce as on how to practice evidence-based medicine in the therapy of diseases.
Objective To evaluate the effectiveness of teaching evidence-based medicine (EBM). Methods 1. Introducing EBM teaching material in Chinese. 2. Offering EBM course in medical college of Sichuan University. 3. Problem-based,self-directed teaching methods. 4. A variety of test method. Results 36 Cochrane systematic review titles were registered, 17 Cochrane systematic review protocols were published in Cochrane Library, 6 Cochrane systematic reviews were published in Cochrane Library. 62 EBM research papers were published on Chinese Journal of EBM. Feedback of teaching EBM from postgraduates: 77.6%, 22.4% postgraduates consider this EBM course is very helpful and helpful for them respectively; 14.3%, 80% postgraduates achieve completely the goal and achieve the goal in greater part by this EBM course respectively; the reason of not achieving the goal is a lack of time to read and attend the course. 61.2%-80% and 16.3%-32.7% postgraduates consider the teaching contents is very good and good respectively; 61.2%-75.5% and 12.3%-28.6% postgraduates consider this teaching model is very good and good respectively; 44.9% postgraduates hope to increase hours of EBM course, increase discusses, increase EBM practice in future; 10.2% postgraduates consider the questions of test are hard to solve. Conclusion This EBM course is effective.
Objective To assess the effectiveness of evidence-based medicine for improving core competencies of undergraduate medical students. Methods MEDLINE, ERIC, Academic Source Premier, Campbell Library databases and three Chinese Databases (CBM, CNKI, VIP) were searched from January 1992 to May 2009.We also used Google to searching related literature. The design is a systematic review of randomized, non-randomized, and before-after studies. Two reviewers did study selection, quality assessment, and data abstraction independently. Different opinions were resolved by consensus. We used an adaptation of the quality measure from Gemma Flores-Mateo et al to assess the quality of selected studies. And descriptive analysis was conducted. Results A total of 17 studies met the selection criteria, 2 of them were of high quality, the others were of moderate quality. Studies involved Competencies of Scientific Foundation of Medicine, Clinical Skills, and Management of Information. Conclusion Competencies of Scientific Foundation of Medicine, Clinical Skills, and Management of Information are improved by evidence-based medicine teaching. No study on professional values, attitudes, behavior and ethics, population health and health systems, management of information, critical thinking and research is available. It is impossible to assess the four domains above.
There are a great number of uncertainties in medical practice, causing considerable difficulties in medical activities such as diagnosis and prognostic prediction. Neural-fuzzy system (NFS) combines the advantages of artificial neural networks and fuzzy logic very well, and has become a new type of artificial intelligence model which is capable of acquiring knowledge from data and expressing it in the form of fuzzy rules. Because of its strong capability of classification and processing fuzzy information, NFS is more and more used in medical practice. Adaptive neural-fuzzy inference system (ANFIS) is one of the most popular forms of NFS. This review focuses on the use of ANFIS in medical practice.
Evidence-based education (EBE) is the integration of professional wisdom and the best available experimental evidence to make educational guiding decisions. EBE aims to improve the scientificity and effectiveness of educational policies, decisions and practices through the combination of evidence-based research and personal professional experience, so as to improve the quality of education and teaching. This paper focuses on the definition, connotation, characteristics, implementation principles and the background of EBE.
ObjectiveTo evaluate the predictive value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with multislice computed tomography (MSCT) in the evaluation of neoadjuvant chemotherapy (NACT) for breast cancer. MethodsThe clinical, imaging, and pathological data of breast cancer patients who received NACT in the Affiliated Hospital of Southwest Medical University from February 2019 to August 2021 were retrospectively collected. Based on the results of postoperative pathological examination, the patients were assigned into significant remission (Miller-Payne grade Ⅰ–Ⅲ) and non-significant remission (Miller-Payne grade Ⅳ–Ⅴ). The variables with statistical significance by univariate analysis or factors with clinical significance judged based on professional knowledge were included to conduct the logistic regression multivariate analysis to screen the risk factors affecting the degree of pathological remission after NACT. Then, the screened risk factors were used to establish a prediction model for the degree of pathological remission of breast cancer after NACT, and the efficacy of this model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve. ResultsAccording to the inclusion and exclusion criteria, a total of 211 breast cancer patients who received NACT were collected, including 116 patients with significant remission and 95 patients with non-significant remission. Logistic regression multivariate analysis results showed that the human epidermal growth factor receptor 2 positive, lower early enhancement rate after NACT, lower arterial stage net increment after NACT, and lower CT value of arterial phase of lesions would increase the probability of significant remission in patients with breast cancer after NACT (P<0.05). The area under the ROC curve of the model for predicting the degree of pathological remission of breast cancer after NACT was 0.984, the specificity was 93.7%, and the sensitivity was 95.7%. The calibration curve showed that the model result fit well with the actual result, and the DCA result showed that it had a high clinical net benefit value. ConclusionFrom the results of this study, DCE-MRI combined with MSCT enhanced scanning has a good predictive value for pathological remission degree after NACT for breast cancer, which can provide clinical guidance for further treatment.