Evidence-based medicine is the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients. The practice of evidence-based medicine means to integrate individual clinical expertise with the best available external clinical evidence from systematic research. So evidence and its quality is the key issue of evidence-based medicine. The purpose of this article is to introduce to the healthcare professionals the sources of evidence and how to search for evidence for them.
Evidence has been retrieved through MEDLINE and Cochrane Libray about the treatment for patients with advanced Parkinson’s disease who suffered from on-off, dyskinesia and depression after chronic use of L-dopa. All of the evidence has been evaluated. Methods of evidence-based treatment were drawn up according to the evidence, clinciams’ experiences and patients’ preferences. All symptoms of the patient have been improved obviously.
ObjectiveTo evaluate the efficacy and safety of all kinds of hemocoagulase on operative incisions. MethodsDatabases including Web of Science, MEDLINE, EMbase, EBSCO, PubMed, CNKI, WanFang Data and VIP were electronically searched to collect randomized controlled trials (RCTs) about hemocoagulase on operative incisions from the inception to June 20th, 2015. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was performed by RevMan 5.2 software. ResultsA total of 16 RCTs involving 1 867 patients were included. The results of meta-analysis showed that, compared with the control group, the hemostatic time (MD=-37.84, 95%CI -52.72 to -22.96, P<0.000 01), blood loss volume per unit area (MD=-0.09, 95%CI -0.10 to -0.07, P<0.000 01), PT of the first postoperative day (MD=-0.37, 95%CI -0.65 to -0.09, P=0.009) were significantly shorter in the hemocoagulase group. However, no significant differences were found in APTT, TT and FIB between two groups. ConclusionHemocoagulase can reduce hemostatic time and blood loss volume in surgical incisions. Due to the limited quantity and quality of the included studies, the above conclusion needs to be further verified by more high quality studies.
In 2014, the new concept of embolic stroke of undetermined source (ESUS) was first proposed by cryptogenic stroke/ESUS International Working Group. In the past 5 years, related clinical researches of ESUS have been deepened, and the results of many large clinical studies have been published. However, the guiding significance of this new concept to clinical practice is still controversial. By reviewing the background, diagnostic criteria, assessment, common emboli sources, anticoagulant therapy research advances and related limitations of ESUS, and analyzing the possible causes of negative anticoagulant therapy results, we explored the clinical value of this new classification.
Objective To study the risk factors of urinary incontinence in acute stroke patients and provide scientific evidence for preventing and managing such complication. Methods A computerized literature search was performed on both English and Chinese databases including Embase, Medline, Wanfang Data, VIP, and CNKI from January 1990 to January 2017 based on such search strategies as literature review and manual retrieval. In addition, we tracked down the related reference lists. The RevMan 5.3 software was used for Meta-analysis. Categorical data were calculated by the pooled odds ratio (OR) values and 95% confidence intervals (CI), and numerical data were calculated by pooled mean difference (MD) and 95%CI. Results A total of 17 articles of controlled studies with 2 428 cases and 3 725 controls were included. According to the results of Meta-analysis, factors associated with urinary incontinence following acute stroke were age [MD=2.80, 95%CI (0.29, 5.30),P=0.03], female gender [OR=1.29, 95%CI (1.16, 1.45),P<0.000 01], diabetes [OR=1.40, 95%CI (1.13, 1.73),P=0.002], heart disease [OR=1.65, 95%CI (1.29, 2.13),P<0.000 1), former cerebrovascular disease [OR=1.43, 95%CI (1.21, 1.69),P<0.000 1), speech disorder [OR=4.20, 95%CI (3.45, 5.10),P<0.000 01], smoking [OR=0.68, 95%CI (0.50, 0.92),P=0.01]. Hypertension [OR=1.25, 95%CI (0.99, 1.58),P=0.06], left hemisphere involvement [OR=1.29, 95%CI (0.81, 2.06),P=0.29], and hemorrhagic stroke [OR=1.26, 95%CI (0.79, 2.03),P=0.33] were not correlated with urinary incontinence following acute stroke. Conclusions Older age, female gender, diabetes, heart disease, former cerebrovascular disease and speech disorder are risk factors associated with post-stroke urinary incontinence, while smoking lowers the potential risk. However, hypertension, hemorrhagic stroke and left hemisphere involvement do not significantly increase the risk of urinary incontinence following stroke.
Randomized controlled trials (RCTs) are the gold standard for the design of clinical trials. Because of some practical difficulties, more and more researchers think that the appropriate use of non-randomized controlled trials may make up for the weakness of RCT and will achieve the same research purpose. Therefore, non-RCTs are also very important. Taking studies on multiple sclerosis for example, this article briefly introduces the significance of non-randomized contolled trials.
The extraction of neuroimaging features of migraine patients and the design of identification models are of great significance for the auxiliary diagnosis of related diseases. Compared with the commonly used image features, this study directly uses time-series signals to characterize the functional state of the brain in migraine patients and healthy controls, which can effectively utilize the temporal information and reduce the computational effort of classification model training. Firstly, Group Independent Component Analysis and Dictionary Learning were used to segment different brain areas for small-sample groups and then the regional average time-series signals were extracted. Next, the extracted time series were divided equally into multiple subseries to expand the model input sample. Finally, the time series were modeled using a bi-directional long-short term memory network to learn the pre-and-post temporal information within each time series to characterize the periodic brain state changes to improve the diagnostic accuracy of migraine. The results showed that the classification accuracy of migraine patients and healthy controls was 96.94%, the area under the curve was 0.98, and the computation time was relatively shorter. The experiments indicate that the method in this paper has strong applicability, and the combination of time-series feature extraction and bi-directional long-short term memory network model can be better used for the classification and diagnosis of migraine. This work provides a new idea for the lightweight diagnostic model based on small-sample neuroimaging data, and contributes to the exploration of the neural discrimination mechanism of related diseases.