Through collecting and synthesizing the paper concerning the method of dealing with heterogeneity in the meta analysis, to introduce the multi-levels statistical models, such as meta regression and baseline risk effect model based on random effects, and random effects model based on hierarchical bayes, and to introduce their application of controlling the meta analysis heterogeneity. The multi-levels statistical model will decompose the single random error in the traditional model to data structure hierarchical. Its fitting effect can not only make the meta-analysis result more robust and reasonable, but also guide clinical issues through the interpretation of association variable.
Objective To establish a risk prediction model of diabetic retinopathy (DR) for type 2 diabetic patients (T2DM). Methods A total of 315 T2DM patients (600 eyes) were enrolled in the study. There were 132 males (264 eyes) and 183 females (366 eyes). The mean age was (67.28±12.17) years and the mean diabetes duration was (10.86±7.81) years. The subjects were randomly assigned to model group and check group, each had 252 patients (504 eyes) and 63 patients (126 eyes) respectively. Some basic information including gender, age, education degree and diabetes duration were collected. The probable risk factors of DR including height, weight, blood pressure, fasting glucose, glycosylated hemoglobin (HbA1c), blood urea, serum creatinine, uric acid, triglyceride, total cholesterol, high-density lipoprotein, low density lipoprotein cholesterol and urinary protein. The fundus photograph and the axial length were measured. Multivariate logistic regression was used to analyze the correlative factors of DR and establish the regression equation (risk model). Receiver operating characteristic (ROC) curves were used to determine the cut-off point for the score. The maximum Youden Index was used to determine the threshold of the equation. The check group was used to check the feasibility of the predictive model. Results Among 504 eyes in the model group, 170 eyes were DR and 334 eyes were not. Among 126 eyes in the check group, 45 eyes were DR and 81 eyes were not. Multivariate logistic regression analysis revealed that axial length [β=–0.196, odds ratio (OR)=0.822,P＜0.001], age (β=-0.079,OR=0.924,P＜0.001), diabetes duration (β=0.048,OR=1.049,P=0.001), HbA1c (β=0.184,OR=1.202,P=0.020), urinary protein (β=1.298,OR=3.661,P＜0.001) were correlated with DR significantly and the simplified calculation of the score of DR were as follows:P=7.018–0.196X1–0.079X2+0.048X3+0.148X4+1.298X5 (X1= axial length, X2=age, X3=diabetes duration, X4=glycosylated hemoglobin, X5= urinary protein). The area under the ROC curve for the score DR was 0.800 and the cut-off point of the score was -1.485. The elements of the check group were substituted into the equation to calculate the scores and the scores were compared with the diagnostic threshold to ensure the patients in high-risk of DR. The result of the score showed 84% sensitivity and 59% specificity. ROC curve for the score to predict DR was 0.756. Conclusion Axial length, age, diabetes duration, HbA1c and urinary protein have significant correlation with DR. The sensitivity and specificity of the risk model to predict DR are 84.0% and 59.0% respectively. The area under the ROC curve was 0.756.
The "bnma" package is a Bayesian network meta-analysis software package developed based on the R programming language. The network meta-analysis was performed utilizing JAGS software, which yielded relevant results and visual graphs. Moreover, this software package provides support for various data structures and types, while also providing the advantages of flexible utilization, user-friendly operation, and deliver of rich and accurate outcomes. In this paper, using a network meta-analysis example of different therapies for androgenetic alopecia, the operational process of conducting network meta-analysis using the "bnma" package is briefly introduced.
In 2025, the American Cancer Society published "Cancer statistics, 2025", which projected cancer data for the upcoming year based on incidence data collected by central cancer registries (through 2021) and mortality data obtained from the National Center for Health Statistics (through 2022). Similarly, the National Cancer Center of China released "Cancer incidence and mortality in China, 2022" in December 2024, analyzing data from 22 cancer registries across the country. This study provides a comparative analysis of cancer incidence and mortality trends in China and the United States during the same period, with a focus on sex- and age-specific distributions and long-term changes in cancer patterns. Long-term trends indicate that lung and liver cancer mortality rates in China have declined, primarily due to tobacco control measures and hepatitis B vaccination programs. However, the burden of gastric and esophageal cancers remains substantial. In the United States, mortality rates for colorectal and lung cancers have continued to decline, largely attributed to widespread screening programs and advances in immunotherapy. As economic growth and social development, China’s cancer profile is gradually shifting towards patterns observed in countries with high human development index. However, the prevention and control of upper gastrointestinal cancers remains a critical public health challenge that requires further attention.
The phase-locking relationship between the firings of neuronal action potentials (i.e., spikes) and the oscillations of local field potentials (LFP) reflects important neural coding information. However, the present analysis methods can only determine whether there has phase-locking, but not the different strengths among various types of phase-locking. In the present paper, we used spike-triggered average (STA) signals and the percentage ratio (named φ) of the STA power to the power of original LFP as an index to evaluate the strengths of phase-locking. Experimental recordings obtained from rat hippocampal CA1 region as well as simulation data were used to evaluate the method. The results showed that the index φ changed monotonically as a function of the strength of phase-locking, and it could provide an effective critical value to divide phase-locking from non-phase-locking. Because the calculation of the index does not need pre-filtering, it can avoid the unwanted influences caused by intentionally limiting the frequencies of LFP oscillations such as in the traditional bin statistical method. Therefore, the index φ provides a novel method to investigate the mechanisms underlying neuronal coding in brain.
There are so many biomechanical risk factors related with glaucoma and their relationship is much complex. This paper reviewed the state-of-the-art research works on glaucoma related mechanical effects. With regards to the development perspectives of studies on glaucoma biomechanics, a completely novel biomechanical evaluation factor -- Fractional Flow Reserve (FPR) for glaucoma was proposed, and developing clinical application oriented glaucoma risk assessment algorithm and application system by using the new techniques such as artificial intelligence and machine learning were suggested.
Objective To explore the knowledge distribution, knowledge clustering, and the trend in development of wound therapy, by revealing the same keywords with multiple statistical method and social network analysis. Methods We searched the CNKI under the term " wound” , " therapy” , and " wound therapy” in February 2016. After the core keywords had been identified by Bicomb and Endnote X6 software in each stage, the co-occurrence matrix was built. Transformation, dimensionality reduction and clustering of the co-occurrence matrix were finished by SPSS 22.0 software, leading the strategic plot to be built. The visualized network images were drawn using Ucinet 6.0 software. Results The visualized domain knowledge-mapping was successfully built, and it directly reflected the structure of knowledge-mapping of the discipline, as well as key clusters. Boost development had been identified in this research. The subject developed own core research areas and clusters, but there was still lack of fitting characteristics. The newly wound therapeutic techniques had limited correlation with other clusters, while provided limited contributions to forward this subject. However, enriched core keywords had been demonstrated, and formed clear domain parts of this subject. Conclusions The analysis demonstrates that wound therapy has developed well, and hot research points follow the direction of medication treatment. The network of wound therapeutic subject has become mature and completed within a short period. Comprehensive therapy and long term follow-up results according to evidence-based nursing have become the domain field. Moreover, the newly therapeutic techniques should be paid more attention to shift the development of this subject. And the interactive research within this subject and among other regions should be enhanced.
Machine learning-based diagnostic tests have certain differences of measurement indicators with traditional diagnostic tests. In this paper, we elaborate the definitions, calculation methods and statistical inferences of common measurement indicators of machine learning-based diagnosis models in detail. We hope that this paper will be helpful for clinical researchers to better evaluate machine learning diagnostic models.
ObjectiveTo establish an appropriate diabetic retinopathy (DR) risk assessment model for patients with type 2 diabetes mellitus (T2DM).MethodsA retrospective clinical analysis. From January 2016 to December 2017, 753 T2DM patients in the Third Affiliated Hospital of Southern Medical University were analyzed retrospectively. Digital fundus photography was taken in all patients. Fasting plasma glucose (FPG), HbA1c, total bilirubin (TB), blood platelet, total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-c), low density lipoprotein cholesterol (LDL-c), apolipoprotein-A (apoA), apolipoprotein-B (apoB), serum creatinine, blood urea nitrogen (BUN), blood uric acid, fibrinogen (Fg), estimated glomerular filtration (eGFR) were collected. The patients were randomly assigned to model group and testify group, each had 702 patients and 51 patients respectively. Logistic regression was used to screen risk factors of DR and develop an assessment scale that can be used to predict DR. Goodness of fit was examined using the Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve.ResultsAmong 702 patients in the model group, 483 patients were DR, 219 patients were NDR. The scores for DR risk were duration of diabetes ≥4.5 years, 4 points; total bilirubin ＜6.65 mol/L, 2 points; apoA≥1.18 g/L, 2 points; blood urea≥6.46 mmol/L, 1 points; HbA1c ≥7.75%, 2 points; HDL-c＜1.38 mmol/L, 2 points; diabetic nephropathy, 3 points; fibrinogen, 1 point. The area under the receiver operating characteristic curve was 0.787. The logistic regression analysis showed that the risk factors independently associated with DR were duration of diabetes (β=1.272, OR=3.569, 95%CI 2.283−5.578, P＜0.001), TB (β=0.744, OR=2.104, 95%CI 1.404−3.152, P＜0.001, BUN (β=0.401, OR=1.494, 95%CI 0.996−2.240, P=0.052), HbA1c (β=0.545, OR=1.724, 95%CI 1.165−2.55, P=0.006), HDL-c (β=0.666, OR=1.986, 95%CI 1.149−3.298, P=0.013), diabetic nephropathy (β=1.151, OR=3.162, 95%CI 2.080−4.806, P=0.013), Fg (β=0.333, OR=1.396, 95%CI 0.945−2.061, P=0.094). The risk model was P=1/[1+exp−(−3.799+1.272X1+0.744X2+0.769X3+0.401X4+0.545X5+0.666X6+1.151X7+0.333X8)]. X1= duration of diabetes, X2=TB, X3=apoA, X4=BUN, X5=HbA1c, X6=HDL-c, X7=diabetic nephropathy, X8=Fg. The area under the ROC curve was 0.787 and the Hosmer-Lemeshow test suggested excellent agreement (χ2=10.125, df=8, P=0.256) in model group. The area under the ROC curve was 0.869 and the Hosmer-Lemeshow test suggested excellent agreement (χ2=5.345, df=7, P=0.618) in model group.ConclusionThe area under the ROC curve for DR was 0.787. The duration of diabetes, TB, BUN, HbA1c, HDL-c, diabetic nephropathy, apoA, Fg are the risk factors of DR in T2DM patients.
Patient-specific volumetric modulated arc therapy (VMAT) quality assurance (QA) process is an important component of the implementation process of clinical radiotherapy. The tolerance limit and action limit of discrepancies between the calculated dose and the delivered radiation dose are the key parts of the VMAT QA processes as recognized by the AAPM TG-218 report, however, there is no unified standard for these two values among radiotherapy centers. In this study, based on the operational recommendations given in the AAPM TG-218 report, treatment site-specific tolerance limits and action limits of gamma pass rate in VMAT QA processes when using ArcCHECK for dose verification were established by statistical process control (SPC) methodology. The tolerance limit and action limit were calculated based on the first 25 in-control VMAT QA for each site. The individual control charts were drawn to continuously monitor the VMAT QA process with 287 VMAT plans and analyze the causes of VMAT QA out of control. The tolerance limits for brain, head and neck, abdomen and pelvic VMAT QA processes were 94.56%, 94.68%, 94.34%, and 92.97%, respectively, and the action limits were 93.82%, 92.54%, 93.23%, and 90.29%, respectively. Except for pelvic, the tolerance limits for the brain, head and neck, and abdomen were close to the universal tolerance limit of TG-218 (95%), and the action limits for all sites were higher than the universal action limit of TG-218 (90%). The out-of-control VMAT QAs were detected by the individual control chart, including one case of head and neck, two of the abdomen and two of the pelvic site. Four of them were affected by the setup error, and one was affected by the calibration of ArcCHECK. The results show that the SPC methodology can effectively monitor the IMRT/VMAT QA processes. Setting treatment site-specific tolerance limits is helpful to investigate the cause of out-of-control VMAT QA.