本文介紹證據質量分級的GRADE方法。GRADE將證據質量分為高、中、低和極低四個級別。該分級應用于證據群，而非針對個別研究。系統評價中，質量反映了我們認為效應估計值正確的把握度。對推薦意見而言，質量反映了我們認為效應估計值足以支持某特定推薦的把握度。隨機對照試驗初定為高質量證據，觀察性研究初定為低質量證據。GRADE中所使用的質量一詞不只是意味著偏倚風險，還可能受研究結果的不精確性、不一致性和間接性，及發表偏倚的影響。此外，若干因素可增加我們對效應估計值的把握度。GRADE提供了一種系統方法來思考并報告各因素。GRADE將評估證據質量的過程與給出推薦建議的過程分開。推薦強度的判斷不僅依賴于證據質量。
Lung cancer is the most threatening tumor disease to human health. Early detection is crucial to improve the survival rate and recovery rate of lung cancer patients. Existing methods use the two-dimensional multi-view framework to learn lung nodules features and simply integrate multi-view features to achieve the classification of benign and malignant lung nodules. However, these methods suffer from the problems of not capturing the spatial features effectively and ignoring the variability of multi-views. Therefore, this paper proposes a three-dimensional (3D) multi-view convolutional neural network (MVCNN) framework. To further solve the problem of different views in the multi-view model, a 3D multi-view squeeze-and-excitation convolution neural network (MVSECNN) model is constructed by introducing the squeeze-and-excitation (SE) module in the feature fusion stage. Finally, statistical methods are used to analyze model predictions and doctor annotations. In the independent test set, the classification accuracy and sensitivity of the model were 96.04% and 98.59% respectively, which were higher than other state-of-the-art methods. The consistency score between the predictions of the model and the pathological diagnosis results was 0.948, which is significantly higher than that between the doctor annotations and the pathological diagnosis results. The methods presented in this paper can effectively learn the spatial heterogeneity of lung nodules and solve the problem of multi-view differences. At the same time, the classification of benign and malignant lung nodules can be achieved, which is of great significance for assisting doctors in clinical diagnosis.
Objective To compare the inter-observer agreement, consistency with the gold standard, and accuracy of the 2007 and 2018 versions of the AO/OTA classification in femoral intertrochanteric fractures, and to identify easily confused fracture types. Methods X-ray images of patients with femoral intertrochanteric fractures at Daping Hospital, Army Medical University between 2017 and 2021 were retrospectively collected. Three senior orthopedic trauma surgeons independently classified the fractures using both the 2007 and 2018 AO/OTA versions. A committee of five experts established the gold standard. Kappa coefficients were used to evaluate inter-observer agreement and consistency with the gold standard, while a confusion matrix was used to analyze accuracy and confusion points. Results A total of 236 patients were included. Regarding inter-observer agreement, the 2007 version was superior to the 2018 version at the subtype level [Kappa value: (0.473-0.739) vs. (0.322-0.658)], with no significant difference at the subgroup level [Kappa value: (0.234-0.453) vs. (0.204-0.442)]. Regarding consistency with the gold standard, the 2018 version was slightly better than the 2007 version [Kappa value: (0.332-0.629) vs. (0.269-0.581)] at the subgroup level. In terms of accuracy, the 2007 version showed higher accuracy at the subtype level (72.50% vs. 70.11%), whereas the 2018 version demonstrated better accuracy at the subgroup level (59.04% vs. 51.99%). The most easily confused subtypes in both versions were A1 and A2. At the subgroup level, A2.2 was the most easily confused type in both versions. Conclusions There is inconsistency in the application of both classification versions by surgeons. The 2007 version demonstrates slightly better inter-observer agreement at the subtype level, while the 2018 version shows better accuracy at the subgroup level. The A2.2 subgroup is a major point of confusion, suggesting that clinical attention should be focused on this type or that auxiliary tools may be needed to improve accuracy.
The gait acquisition system can be used for gait analysis. The traditional wearable gait acquisition system will lead to large errors in gait parameters due to different wearing positions of sensors. The gait acquisition system based on marker method is expensive and needs to be used by combining with the force measurement system under the guidance of rehabilitation doctors. Due to the complex operation, it is inconvenient for clinical application. In this paper, a gait signal acquisition system that combines foot pressure detection and Azure Kinect system is designed. Fifteen subjects are organized to participate in gait test, and relevant data are collected. The calculation method of gait spatiotemporal parameters and joint angle parameters is proposed, and the consistency analysis and error analysis of the gait parameters of proposed system and camera marking method are carried out. The results show that the parameters obtained by the two systems have good consistency (Pearson correlation coefficient r ≥ 0.9, P < 0.05) and have small error (root mean square error of gait parameters is less than 0.1, root mean square error of joint angle parameters is less than 6). In conclusion, the gait acquisition system and its parameter extraction method proposed in this paper can provide reliable data acquisition results as a theoretical basis for gait feature analysis in clinical medicine.
Objectives To evaluate the reporting quality of Bland-Altman method consistency evaluation in China from 2014 to 2016. Methods WanFang Data, VIP and CNKI databases were electronically searched to collect literature about the application of Bland-Altman method from 2014 to 2016 in China. Two reviewers screened literature, extracted data, and the data were then statistically analyzed by SPSS 22.0 software. Results A total of 376 articles were included. The published articles on Bland-Altman method had major flaws (not conforming to reporting standards) in the application conditions, evaluation indexes, graphic depiction and so on. Merely 11.4% of the literature set the clinically acceptable consensus values in the pre-period studies. Merely one literature (0.3%) correctly compared the 95%CI of 95%LoA with the clinically acceptable threshold which had been set previously. The offer rates of the differences between the two measurements and the 95%CI, 95%LoA and 95%CI of 95%LoA in the figure were 95.9%, 9.5%, 94.6% and 4.4%, respectively. Conclusions The reporting quality of Bland-Altman method consistency evaluation in China is of low quality, specifically not conforming to reporting standards. We should strengthen the introduction of Bland-Altman methodology to improve the reporting quality.
The aim of this paper is to reveal the change of the brain function for nicotine addicts after smoking cessation, and explore the basis of neural physiology for the nicotine addicts in the process of smoking cessation. Fourteen subjects, who have a strong dependence on nicotine, have agreed to give up smoking and insist on completing the test, and 11 volunteers were recruited as the controls. The resting state functional magnetic resonance imaging and the regional homogeneity (ReHo) algorithm have been used to study the neural activity before and after smoking cessation. A two factors mixed design was used to investigate within-group effects and between-group effects. After 2 weeks’ smoking cessation, the increased ReHo value were exhibited in the brain area of supplementary motor area, paracentral lobule, calcarine, cuneus and lingual gyrus. It suggested that the synchronization of neural activity was enhanced in these brain areas. And between-group interaction effects were appeared in supplementary motor area, paracentral lobule, precentral gyrus, postcentral gyrus, and superior frontal gyrus. The results indicate that the brain function in supplementary motor area of smoking addicts would be enhanced significantly after 2 weeks’ smoking cessation.
Compared with traditional head to head meta-analysis, network meta-analysis has more confounding factors and difficulties to handle. Due to the mutual transitivity of evidence in network meta-analysis, heterogeneity may be brought into indirect meta-analysis. Hence, effective differentiation and correct handling of heterogeneity are being current focus. In order to ensure the reliability of the results of network meta-analysis, the concept of homogeneity is proposed and a series of methods are developed for differentiation and handling of homogeneity. Based on the extension of Bucher methods, current methods for differentiation and handling of homogeneity has extended to ten quantitative measures (eg., node analysis method, hypothesis tests, and two-step method). However, because of the differences and the focus of fundamental methodological theories as well as the limitation of statistics power, no highly-effective method has been worked out. Therefore, the exploration of highly-effective, simple and high-resolved methods are still needed.
Objective To investigate an evaluation method of medical literature applicability to clinical work， and provide a convenient way for physicians to search for the best evidence. Methods Delphi method was used to choose appropriate evaluating indexes， analytic hierarchy process was performed to determine the weighing of each index， and the formula to calculate medical literature applicability was formed. The practicability of this formula was evaluated by consistency checking between the formula’s results and experts’ opinions on literature applicability. Results Five evaluating indexes were determined， including literature’s publishing year （X1）， whether the target questions were covered （X2）, sample size （X3）， trial category （X4）， and journal level （X5）. The formula to calculate medical literature applicability was Y=3.93 X1＋11.78 X2＋14.83 X3＋44.53 X4＋24.93 X5. The result of consistency checking showed that the formula’s results were highly consistent with experts’ opinions （Kappa=0.75， P＜0.001）. Conclusion The applicability formula is a valuable tool to evaluate medical literature applicability.
目的 利用局部一致性（ReHo）方法探測創傷后應激障礙（PTSD）患者在靜息狀態下是否存在著大腦功能異常。 方法 2010年5月－7月對18例未經治療的地震PTSD患者和19例同樣經歷地震但未患PTSD的對照者進行了靜息態功能磁共振成像（Rs-fMRI） 掃描。應用ReHo方法處理Rs-fMRI數據，得出PTSD患者的異常腦區，并將患者存在組間差異的腦區ReHo值與臨床用PTSD診斷量表（CAPS）、漢密爾頓抑郁量表（HAMD）和漢密爾頓焦慮量表（HAMA）分別進行相關分析。 結果 ① PTSD組ReHo顯著增加的腦區包括右側顳下回、楔前葉、頂下葉、中扣帶回，左側枕中回以及左/右側后扣帶回；ReHo顯著降低的腦區包括左側海馬和左/右側腹側前扣帶回。② 異常腦區中后扣帶回和右側中扣帶回ReHo與HAMD呈負相關（中扣帶回r=?0.575，P=0.012；右側后扣帶回：r=?0.507，P=0.032），其余腦區ReHo與臨床指標無明顯相關性（P＞0.05），左側海馬與CAPS的相關性相對其他腦區較大（r=?0.430，P=0.075）。 結論 PTSD患者在靜息狀態下即存在著局部腦功能活動的降低和增加，ReHo方法可能有助于研究PTSD患者靜息狀態腦活動。