With the aging of the population, the incidence of benign paroxysmal positional vertigo (BPPV) and osteoporosis have been increasing year by year, and the incidence of BPPV in vertigo related diseases has also been ranked first. There are similarities in structure, formation and metabolic mechanism between bone and otolith, but there is no consistent conclusion on the relationship between BPPV and osteoporosis. This article summarizes the current situation of the research on the correlation between BPPV and osteoporosis, the common risk factors and the related co-occurring mechanisms, aiming to provide more ideas for the prevention and treatment of BPPV patients, and improve the prevention and treatment ability of the co-diseases in the elderly.
The medical literature contains a wealth of valuable medical knowledge. At present, the research on extraction of entity relationship in medical literature has made great progress, but with the exponential increase in the number of medical literature, the annotation of medical text has become a big problem. In order to solve the problem of manual annotation time such as consuming and heavy workload, a remote monitoring annotation method is proposed, but this method will introduce a lot of noise. In this paper, a novel neural network structure based on convolutional neural network is proposed, which can solve a large number of noise problems. The model can use the multi-window convolutional neural network to automatically extract sentence features. After the sentence vectors are obtained, the sentences that are effective to the real relationship are selected through the attention mechanism. In particular, an entity type (ET) embedding method is proposed for relationship classification by adding entity type characteristics. The attention mechanism at sentence level is proposed for relation extraction in allusion to the unavoidable labeling errors in training texts. We conducted an experiment using 968 medical references on diabetes, and the results showed that compared with the baseline model, the present model achieved good results in the medical literature, and F1-score reached 93.15%. Finally, the extracted 11 types of relationships were stored as triples, and these triples were used to create a medical map of complex relationships with 33 347 nodes and 43 686 relationship edges. Experimental results show that the algorithm used in this paper is superior to the optimal reference system for relationship extraction.
Objective To analyze the potential causal relationship between sunscreen/ultraviolet protection and the risk of non-Hodgkin lymphoma using a two sample Mendelian randomization (MR) study method. Methods The summary data of genome-wide association study was used to select three types of non-Hodgkin lymphoma, namely diffuse large B-cell lymphoma (DLBCL), follicular lymphoma, T/NK cell lymphoma, and sunscreen/ultraviolet protection highly correlated genetic loci, namely single nucleotide polymorphism (SNP), as instrumental variables. The reverse variance weighting method was used as the main method for MR analysis, MR Egger and MR-PRESO were used to detect level pleiotropy, and leave-one-out method was used for sensitivity analysis to ensure the robustness of the results. Results A total of 132 SNPs were included in the analysis. The results of the inverse variance weighted analysis showed that sunscreen/ultraviolet protection increased the incidence of DLBCL [odds ratio=2.439, 95% confidence interval (1.109, 5.362), P=0.027]. The heterogeneity test results showed that there was no heterogeneity in the causal relationship between sunscreen/ultraviolet protection and DLBCL (P>0.05). The results of the horizontal pleiotropy test showed that SNP did not exhibit horizontal pleiotropy (P>0.05). The leave-one-out method showed that no SNP with a significant impact on the results was found. There was no causal relationship between sunscreen/ultraviolet protection and follicular lymphoma and T/NK cell lymphoma. Conclusion There is a positive causal relationship between sunscreen/ultraviolet protection and the incidence of DLBCL.
To analyze the current doctor-patient relationship and explore its underlying reasons. Evidence-based medicine may provide scientific evidence for the deepening of healthcare reforms as well as the improvement of social security system; provide abundant information for both sides of the doctor-patient relationship; improve medical quality and reduce medical costs, so as to establish a harmonious patient-oriented doctor-patient relationship .
Objective To explore the factors which affect shared decision-making and develop strategies to get patients actively involved in clinical decision-making. Methods We conducted a survey on 566 patients of a Class A Hospital in Sichuan with group random sampling method. The data were collected by the use of anonymous selfadministered questionnaires. We used SPSS 10.0 to analyse the data. Results A total of 600 questionnaires were distributed at random, of which 565 were completed. There were 68% patients who had some knowledge of the disease, and 93% who were willing to participate in clinical decision-making. The patients’ biggest concerns were: treatment effect, cost and doctors’ skills. The biggest difficulties that patients worried about were: long-time waiting in out-patient departments and limited time to communicate with doctors. Conclusion As more and more patients would like to involve in shared decision-making, doctors need to provide patients with more choices and help them make a right decision in their treatment.
According to the heterogeneity between dose-response data across different studies and the potential nonlinear trend within the dose-response relationship, there are several models for trend estimation from summarized dose-response data, with applications to meta-analysis. However, up to now, there is no guideline of conducting a metaanalysis of dose-response data. After summarizing the previous papers, this paper focuses on how to select the right model for conducting a meta-analysis of dose-response data based on the heterogeneity across different studies, the goodness of fit, and the P value of overall association between exposure and event. Then a preliminary statistical process of conducting a meta-analysis of dose-response data is proposed.
Does-response meta-analysis, which has being developed for more than 30 years, is a type of regression function and can be both linear and non-linear model. It plays an important role in investigating the relationship between dependent and independent variable. With its special advantages, dose-response meta-analysis has been widely used in evidence-based practice and decision. Currently there are several models can be used to perform dose-response metaanalysis with various advantages and disadvantages. It is vital to choose best model to perform dose-response metaanalysis in evidence-based practice. In this paper, we briefly introduce and summarize the methodology of dose-response meta-analysis.
ObjectiveTo investigate the causal relationship between gastroesophageal reflux disease (GERD) and obstructive sleep apnea (OSA) with its typical symptoms (snoring and daytime sleepiness) by using Mendelian randomization (MR). MethodsThe inverse-variance weighted method was used as the main analysis method to assess the causal effect. Sensitivity and pleiotropy analyses were carried out using leave-one-out and MR-Egger analysis, and then heterogeneity tests were conducted. ResultsIn the MR analysis, genetically predicted GERD was associated with a greater risk of OSA (IVW: OR=1.528, 95%CI 1.374 to 1.699, P=5.315E‒15). Additional MR results were consistent with the IVW results, and no pleiotropy or heterogeneity was found. We also discovered a significant causal relationship between GRED and snoring (IVW: OR=0.959, 95%CI 0.949 to 0.969, P=1.507E‒15), and daytime sleepiness (IVW: OR=1.024, 95%CI 1.021 to 1.036, P=4.580E‒5), with no evidence of pleiotropy. ConclusionThe MR study supports a causal effect between GERD and OSA with its typical symptoms (daytime sleepiness and snoring).
ObjectiveTo systematically evaluate the dose-response relationship between coffee consumption and liver cancer risk. MethodsThe PubMed, Web of Science, Cochrane Library, EMbase, CNKI, VIP, WanFang Data, and CBM databases were searched from inception to December 2022. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using Stata 17.0 software. ResultsFifteen studies (11 cohort studies and 4 case-control studies) involving 557 259 participants were included. The results of meta-analysis showed that coffee consumption was significantly negatively associated with the risk of liver cancer (RR=0.39, 95%CI 0.27 to 0.57, P<0.01). The dose-response meta-analysis showed a non-linear dose-response relationship between coffee consumption and the risk of liver cancer (P<0.01). Compared with people who did not drink coffee, people who drank 1 cup of coffee a day had a 25% lower risk of liver cancer (RR=0.75, 95%CI 0.67 to 0.83), and people who drank 2 cups of coffee a day had a 38% lower risk of liver cancer (RR=0.62, 95%CI 0.56 to 0.70). The risk of liver cancer decreased by 45% (RR=0.55, 95%CI 0.48 to 0.62) for 3 cups of coffee and by 51% (RR=0.49, 95%CI 0.43 to 0.56) for 4 cups of coffee. ConclusionCurrent evidence suggests that there is a nonlinear dose-response relationship between coffee consumption and the risk of liver cancer. These results indicate that habitual coffee consumption is a protective factor for liver cancer. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
As a valid method in systematic review, dose-response meta-analysis is widely used in investigating the relationship between independent variable and dependent variable, and which usually based on observational studies. With large sample size, observational studies can provide a reasonable amount of statistical power for meta-analysis. However, due to the design defects of observational studies, they tend to introduce many kinds of biases, which may influence the final results that make them deviation from the truth. Given the dead zone of methodology, there is no any bias adjusting method in dose-response meta-analysis. In this article, we will introduce some bias adjusting methods from other observational-study-based meta-analysis and make them suit for dose-response meta-analysis, and then compare the advantages and disadvantages of these methods.