A whole process damage constitutive model for layered sandstone under uniaxial compression based on Logistic function

Bedding structural planes significantly influence the mechanical properties and stability of engineering rock masses.This study conducts uniaxial compression tests on layered sandstone with various bedding angles(0°,15°,30°,45°,60°,75°and 90°)to explore the impact of bedding angle on the deformational mechanical response,failure mode,and damage evolution processes of rocks.It develops a damage model based on the Logistic equation derived from the modulus’s degradation considering the combined effect of the sandstone bedding dip angle and load.This model is employed to study the damage accumulation state and its evolution within the layered rock mass.This research also introduces a piecewise constitutive model that considers the initial compaction characteristics to simulate the whole deformation process of layered sandstone under uniaxial compression.The results revealed that as the bedding angle increases from 0°to 90°,the uniaxial compressive strength and elastic modulus of layered sandstone significantly decrease,slightly increase,and then decline again.The corresponding failure modes transition from splitting tensile failure to slipping shear failure and back to splitting tensile failure.As indicated by the modulus’s degradation,the damage characteristics can be categorized into four stages:initial no damage,damage initiation,damage acceleration,and damage deceleration termination.The theoretical damage model based on the Logistic equation effectively simulates and predicts the entire damage evolution process.Moreover,the theoretical constitutive model curves closely align with the actual stress−strain curves of layered sandstone under uniaxial compression.The introduced constitutive model is concise,with fewer parameters,a straightforward parameter determination process,and a clear physical interpretation.This study offers valuable insights into the theory of layered rock mechanics and holds implications for ensuring the safety of rock engineering.

A comparison of model choice strategies for logistic regression

Purpose:The purpose of this study is to develop and compare model choice strategies in context of logistic regression.Model choice means the choice of the covariates to be included in the model.Design/methodology/approach:The study is based on Monte Carlo simulations.The methods are compared in terms of three measures of accuracy:specificity and two kinds of sensitivity.A loss function combining sensitivity and specificity is introduced and used for a final comparison.Findings:The choice of method depends on how much the users emphasize sensitivity against specificity.It also depends on the sample size.For a typical logistic regression setting with a moderate sample size and a small to moderate effect size,either BIC,BICc or Lasso seems to be optimal.Research limitations:Numerical simulations cannot cover the whole range of data-generating processes occurring with real-world data.Thus,more simulations are needed.Practical implications:Researchers can refer to these results if they believe that their data-generating process is somewhat similar to some of the scenarios presented in this paper.Alternatively,they could run their own simulations and calculate the loss function.Originality/value:This is a systematic comparison of model choice algorithms and heuristics in context of logistic regression.The distinction between two types of sensitivity and a comparison based on a loss function are methodological novelties.

Enhancing PDF Malware Detection through Logistic Model Trees

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection approaches.The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies.The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree.Using a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision Tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient Descent.Beyond traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware detection.The use of Logistic Model Tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus detection.The paper emphasizes Logistic Model Tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector.The results reveal that the Logistic Model Tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

Utilization of Logistical Regression to the Modified Sine-Gordon Model in the MST Experiment

In this paper, a logistical regression statistical analysis (LR) is presented for a set of variables used in experimental measurements in reversed field pinch (RFP) machines, commonly known as “slinky mode” (SM), observed to travel around the torus in Madison Symmetric Torus (MST). The LR analysis is used to utilize the modified Sine-Gordon dynamic equation model to predict with high confidence whether the slinky mode will lock or not lock when compared to the experimentally measured motion of the slinky mode. It is observed that under certain conditions, the slinky mode “locks” at or near the intersection of poloidal and/or toroidal gaps in MST. However, locked mode cease to travel around the torus;while unlocked mode keeps traveling without a change in the energy, making it hard to determine an exact set of conditions to predict locking/unlocking behaviour. The significant key model parameters determined by LR analysis are shown to improve the Sine-Gordon model’s ability to determine the locking/unlocking of magnetohydrodyamic (MHD) modes. The LR analysis of measured variables provides high confidence in anticipating locking versus unlocking of slinky mode proven by relational comparisons between simulations and the experimentally measured motion of the slinky mode in MST.

Impulsive Logistic Model for Gray Leaf Spots Caused by Cercospora zeae-maydi

[ Objectlve] Impulsive Logistic Model was used to simulate epidemic process of Gray Leaf Spots caused by C. zeae-maydi. [ Method] The pathogen was inoculated in different maize varieties, and the incidence were observed and recorded. Impulsive Logistic Model was used to simulate the development process of the disease, which was compared with actual incidence. [ Result] Artificial inoculation tests showed that impulsive Logistic Model could reflect time dynamic of C. zeae-maydi. Through derivation, exponential growth phase was from maize seedling emergence to eady July in each year, logistic phase was from early July to late August, terminal phase was from eady September to the end of maize growth stage. [ Conclusion] The derivation result from model was consistent with the development biological laws of C. zeae-maydi.

决策树和Logistic回归模型对体外受精-胚胎移植患者妊娠结局的预测价值比较

目的:比较决策树和Logistic回归模型对体外受精-胚胎移植(in vitro fertilization and embryo transfer,IVF-ET)患者妊娠结局的预测价值。方法:纳入2021年1月至2022年10月在长治医学院附属和平医院接受IVF-ET的患者350例为研究对象,根据妊娠结局分为妊娠成功组(215例)和妊娠失败组(135例)。收集患者临床资料,建立IVF-ET患者妊娠结局Logistic回归和决策树预测模型,并在是否基于Logistic回归结果条件下建立决策树分析模型(决策树1和决策树2),采用受试者工作特征(receiver operating characteristic,ROC)曲线对模型预测效果进行评价。结果:350例患者中,妊娠成功患者占61.43%,妊娠失败者占38.57%。妊娠失败组年龄≥35岁、不孕年限≥5年、周期次数≥1次、有心理精神障碍的患者比例及HCG日血清孕酮水平均高于妊娠成功组,获卵数≥10枚、受精率≥75%的患者比例及HCG日子宫内膜厚度、优质胚胎数小于妊娠成功组(P<0.05)。多因素Logistic回归分析结果显示,年龄、HCG日血清孕酮水平、优质胚胎数及心理精神障碍均是IVF-ET患者妊娠结局的影响因素(P<0.05)。决策树模型显示,年龄、HCG日血清孕酮水平、优质胚胎数为IVF-ET患者妊娠结局的影响因素。Logistic回归模型曲线下面积(area under curve,AUC)为0.832,预测敏感度、特异度和准确度分别为87.3%、71.4%、83.5%;决策树1的AUC为0.859,预测敏感度、特异度和准确度分别为85.1%、76.8%、85.6%;决策树2的AUC为0.820,预测敏感度、特异度和准确度分别为83.7%、73.2%、82.4%。决策树1的AUC大于决策树2(P<0.05),但与Logistic回归模型的AUC比较差异无统计学意义(P>0.05)。结论:Logistic回归模型和决策树模型对于IVF-ET患者妊娠结局均有一定的预测价值。

X线联合超声Logistic模型预测乳腺癌腋窝淋巴结转移的价值分析

目的分析与乳腺癌腋窝淋巴结转移相关的X线和超声征象,构建Logistic回归模型并评估其对术前预测腋窝淋巴结状态的临床价值。方法选取2015年1月至2022年1月高密市人民医院收治的312例原发性乳腺癌患者作为研究对象,根据是否发生腋窝淋巴结转移(ALNM)分为转移组(n=141)与未转移组(n=171)。所有患者均行X线及超声检查,比较未转移组与转移组乳腺浸润性导管癌的X线征象、超声征象,采用多因素Logistic回归分析ALNM的影响因素;绘制ROC曲线分析X线、超神征象及Logistic回归模型预测乳腺癌腋窝淋巴结转移的价值。结果两组X线原发灶长径、皮肤增厚、乳头回缩、淋巴结门和淋巴结密度比较差异有统计学意义(P<0.05),两组象限位置、钙化和边缘毛刺比较差异无统计学意义;两组超声原发灶长径、淋巴结皮髓质分界和淋巴结皮质比较差异有统计学意义(P<0.05),两组原发灶高回声晕、后场回声、血流分级和纵横比比较差异无统计学意义。多因素Logistic回归分析结果显示,X线征象的乳房皮肤增厚征象、超声淋巴结皮质增厚征象是乳腺浸润性导管癌患者发生ALNM的独立危险因素(P<0.05)。ROC曲线分析结果显示,X线乳房皮肤增厚征象和超声征象的淋巴结皮质增厚预测淋巴结转移的AUC分别为0.652(95%CI:0.589~0.714)、0.725(95%CI:0.666~0.784),模型预测ALNM的AUC为0.795(95%CI:0.742~0.848),预测效能较好。结论乳腺癌患者的X线皮肤增厚征象和超声腋窝淋巴结皮质增厚征象与ALNM有关,X线联合超声的Logistic模型可较准确地预测乳腺癌患者的腋窝淋巴结状态。

基于Logistic模型和SPC算法的关键技术识别与预测——以卫星互联网为例

识别关键技术有助于在制定技术和市场战略布局中获得先发优势,对我国经济发展、国家安全具有重要意义。文中提出一种融合生命周期与主路径分析的关键技术识别方法,是对已有的研究理论和方法的重要补充。首先,检索相关领域专利数据并进行技术划分;然后,综合运用Lo-gistic模型和SPC算法构建技术轨道,识别关键技术;接着,使用显性技术优势指数比较主要国家技术竞争态势;最后,结合关键技术与技术竞争态势绘制技术路线图,识别技术热点与前沿预测。以卫星互联网为例,阐述该方法的应用过程。研究结果表明,该方法能够较为准确识别出该领域关键技术,揭示全球技术竞争态势,有效实现技术热点识别和未来技术预测;识别出的卫星互联网关键技术,能够为国家发展政策制定与相关产业布局提供参考。

社会融合视角下农民工城市定居意愿探赜——基于Logistic模型的实证研究

农民工的城市定居意愿影响农民工市民化进程,进而影响我国新型城镇化建设与乡村振兴。文章基于2023年873份农民工问卷,在社会融合视角下,构建二元logistic回归模型对农民工的城市定居意愿影响因素进行研究。实证研究表明,只有58%的农民工具有城市定居意愿。农民工性别、年龄、婚姻状况、教育水平、家庭随迁人口数量、城市迁移时间长短等因素对定居意愿均没有显著影响;参加本地医保、身份认同、经济收入、健康状况、承包地等因素对农民工的城市定居意愿影响较大;社会参与度、闲暇生活、居住证等在一定程度上提升农民工的定居意愿。如何持续提升农民工的城市定居意愿,文章从社会关系融合、经济收入及公共服务均等化等三个方面提出了对策建议。

基于Logistic回归的国际时尚品牌销售渠道选择影响因素分析

为了帮助国际时尚品牌合理、科学地选择销售渠道和制定销售策略,以满足不同地区不同类型消费者的需求,提高品牌市场竞争力。首先依据访谈数据确定分析因子,建立影响国际时尚品牌销售渠道选择的多因素分析理论模型,提出可能影响国际品牌销售渠道选择的消费者偏好分析指标体系,从消费者个人特征、心理偏好、品类偏好以及时尚认知4个维度设计问卷并进行调研。运用SPSS软件对问卷回收数据进行录入,并采用Logistic回归分析数据,从而验证模型假设。通过分析结果可知,以上4个维度下的诸多影响因子均对国际品牌销售渠道的选择产生显著影响,并给出了具体的指导策略。品牌商可以根据研究结果制定对应的销售策略,以有效提高销售渠道利用率,最大化提升品牌销售业绩。

贝叶斯Logistic回归模型在中老年人心脏病影响因素分析中的应用

目的探讨贝叶斯Logistic回归模型在心脏病影响因素分析研究中的应用价值。方法数据资料来自2015年中国健康与养老追踪调查中的525例调查对象。利用OpenBUGS软件分别拟合了贝叶斯随机效应和固定效应的Logistic回归模型,并在两种模型中估计各影响因素与因变量关系的优势比(OR)及95%可信区间(95%CI)。结果贝叶斯随机效应和固定效应的Logistic回归模型分析结果均显示,性别、高血压和糖尿病是心脏病患病率的影响因素。两个模型的收敛效果均较好,参数估计结果也相差较小,但随机效应模型的拟合效果略差于固定效应模型(随机效应模型:DIC=156.6,pD=11.96;固定效应模型:DIC=155.8,pD=7.79)。结论在贝叶斯Logistic回归模型中引入随机效应参数需根据具体情况而定,否则反而可能会降低模型的拟合效果。

基于Logistic模型的差分隐私推荐算法

针对大数据环境下的用户隐私保护问题,提出一种基于Logistic模型的差分隐私推荐算法。首先,改进了差分隐私保护下的系统模型,用户对自身数据进行编码后发送给可信的服务器进行数据加扰处理,可信的服务器迭代处理后再推送给分析服务器进行推荐。系统确保了用户的数据只能被可信的服务器进行搜集整理,不会泄露用户隐私。其次,基于Logistic模型给出了差分隐私推荐算法,该算法将用户的数据作为一个整体进行分析,提高了效率。

基于多元Logistic模型与解释结构模型的乡村旅游可持续发展路径研究

在全面推进乡村振兴战略背景下,乡村旅游成为助推乡村经济高质量的有效路径。基于广西阳朔县23个景区的1005份问卷数据,采用多元Logistic回归模型和解释结构模型,探讨乡村旅游可持续发展因素及内在逻辑关系。研究发现:①旅游资源多样性、特色民俗文化、交通通达度、投资主体结构、管理者的战略视野和进取态度、国家和地方的政策环境都对乡村旅游的可持续性产生显著影响;②旅游资源的多样性和特色民俗文化是影响乡村旅游可持续发展的深层因素,交通通达度和投资主体结构为中层间接因素,而管理者的战略眼光和进取态度、国家及地方政策环境则是直接影响的表层因素;③乡村旅游的可持续发展是由客观环境因素和主观人为因素共同作用的复杂系统,在动态互动中形成了“旅游资源—发展条件—治理环境—乡村旅游的可持续发展”的传导关系。

Logistic回归模型和XGBoost模型对急性缺血性脑卒中患者发生吞咽障碍的预测价值

目的筛选危险因素构建急性缺血性脑卒中后吞咽障碍风险预测模型,对比XGBoost模型和Logistic回归模型的优劣性。方法选取2022年1-12月新疆医科大学第二附属医院神经内科573例急性缺血性脑卒中患者,按7∶3比例随机分为建模组(n=401)和验证组(n=172)。筛选发生吞咽障碍的危险因素,以单因素分析有统计学意义的变量分别建立Logistic回归模型和XGBoost模型。在验证组数据集上使用十折交叉验证法进行内部验证,采用校准曲线、受试者工作特征曲线(ROC曲线)和决策曲线评价两种模型的预测效能。结果多因素Logistic回归分析结果显示,年龄、NIHSS评分、GCS评分、BI指数、脑干病变、构音障碍、失语症、咽反射(正常)是急性缺血性脑卒中后吞咽障碍的影响因素。XGBoost模型特征重要性排序前8位分别为年龄、BI指数、NIHSS评分、咽反射、TOAST分型、白蛋白、文化程度、营养评分。对比两种模型结果显示,XGBoost模型的准确性、精确度、敏感度、F1分值分别为0.849、0.830、0.754、0.790,表现优于Logistic回归模型。Logistic回归、XGBoost模型预测吞咽障碍的AUC值分别是0.894、0.925,两者AUC值比较,差异无统计学意义(P>0.05)。模型的校准曲线和临床决策曲线均显示XGBoost模型准确度和临床实用价值优于Logistic回归模型。结论XGBoost模型和Logistic回归模型均能有效预测急性缺血性脑卒中后吞咽障碍风险,XGBoost模型表现更优,可为临床早期预防急性缺血性脑卒中吞咽障碍提供参考。

随机森林模型和Logistic回归模型预测非计划再手术发生风险的效能比较

目的比较随机森林模型和Logistic回归模型预测非计划再手术发生风险的效能。方法在手术麻醉系统中筛选一次住院期间申请2次手术的患者信息。提取所有非计划再次手术患者(n=219)作为研究组,对应科室的计划再次手术患者(n=14311)作为对照组。运用随机森林模型和Logistic回归模型建立非计划再手术预测模型。采用受试者工作特征曲线下面积评价两种模型的预测效能。结果(1)Logistic回归分析结果显示,前次术中输血、罹患恶性肿瘤、合并疾病数量、前次手术切口愈合等级、前次手术级别、前次手术时长、前次手术切口类别是非计划再手术发生的影响因素(P<0.05)。Logistic回归预测模型的曲线下面积为0.922,灵敏度、特异度、准确率分别为92.59%、79.11%、79.28%。(2)随机森林模型特征变量的重要性排序结果显示,前次手术切口类别、前次术中输血、前次手术级别、前次手术切口愈合等级、合并疾病数量、罹患恶性肿瘤等变量的重要性更靠前。随机森林预测模型的曲线下面积为0.866,灵敏度、特异度、准确率分别为80.00%、93.33%、86.66%。Logistic回归预测模型曲线下面积大于随机森林预测模型,但差异无统计学意义(P>0.05)。结论综合使用Logistic回归模型和随机森林模型,并将二者分析结果互为补充,可从各个方面预测非计划再次手术的风险因素,能获得更好的预测效能。

基于数据集重构与Logistic回归的林火预测研究

以大兴安岭地区呼玛县为例,采用过采样、欠采样、混合采样以及人工合成等数据平衡方法对林火数据集进行重构,然后分别应用二项Logistic回归模型进行拟合。选择平均预测效果最佳的数据平衡方法,对全样本数据进行重构和建立模型,同时利用标准化回归系数法对各气象因子的相对重要性进行研究。结果显示,综合随机划分的10个样本共计40个平衡数据集,就拟合优度来说,过采样、混合采样和人工合成数据集差别不大,三者略大于欠采样数据集;就预测精度来说,过采样数据集﹥混合采样数据集﹥人工合成数据集﹥欠采样数据集,其中前三者差别不大,但明显大于欠采样数据集。计算各气象因子的标准化回归系数结果,FFMC、日最小相对湿度、DC绝对值最大,分别为0.771、0.720、0.527。本文通过过采样、混合采样及人工合成数据方法所得平衡数据集的拟合效果较好,其中以过采样数据集平均预测准确率最高,可用于该地区林火数据集重构及建立模型,各气象因子中,FFMC、日最小相对湿度、DC是影响研究区林火发生的主要驱动气象因子。

Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed,Gansu Province,China

Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups,(i) training dataset and(ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages,distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.

基于Logistic回归模型和PCA模型的急性缺血性脑卒中发作影响因素分析

目的应用Logistic回归模型和主成分分析(PCA)模型分析急性缺血性脑卒中(AIS)发作的影响因素。方法从大连医科大学附属第二医院医渡云科研大数据服务器系统提取2001年1月1日至2021年12月31日的数据,将医院病历系统收录的55620例AIS患者纳入病例组,将筛选后的64134例在医院体检中心体检的人群纳入对照组。收集两组的临床资料,分析AIS发作的影响因素。结果多因素分析结果显示,年龄、性别、肌酐、白细胞计数、血红蛋白、红细胞计数、血小板计数、甘油三酯(TG)、低密度脂蛋白胆固醇(LDL-C)、高密度脂蛋白胆固醇(HDL-C)、丙氨酸氨基转移酶(ALT)、总胆固醇(TC)、γ-谷氨酰转移酶(γ-GGT)水平及吸烟史、饮酒史、高血压、糖尿病、心梗、冠心病、动脉粥样硬化为AIS的影响因素(P<0.05);受试者工作特征(ROC)曲线下面积(AUC)为0.927。PCA提取8个主成分,既往病史、血脂水平风险比较大。结论既往病史及血脂水平是AIS发作的主要影响因素,有效控制原发疾病及血脂水平能够更好控制AIS发作。

基于logistic回归模型分析骨科手术部位感染的影响因素

目的总结骨科手术部位感染现状,探讨骨科手术部位感染的影响因素。方法收集江苏省盐城市第三人民医院2022年1月至2023年12月骨科手术部位感染病例作为感染组,共41例,按照1∶1的比例,随机抽取同科室2022年1月至2023年12月年龄相近(±10岁)、性别相同未发生手术部位感染的41例病例作为非感染组,分析骨科手术部位感染病原菌分布情况并基于二分类logistic回归模型分析骨科手术部位感染的影响因素。结果本研究共纳入41例手术部位感染患者,术后1~3 d发生手术部位感染10例(24.39%),术后4~6 d发生手术部位感染12例(29.27%),术后7~9 d发生手术部位感染5例(12.20%),术后10~30 d发生手术部位感染10例(24.39%),术后31~90 d发生手术部位感染4例(9.76%)。感染组检出40株病原菌,主要为革兰氏阳性球菌(31株,77.50%),最常见的为金黄色葡萄球菌(19株,47.50%),其次为凝固酶阴性葡萄球菌(7株,17.50%)。两组糖尿病史、体重指数(BMI)、术前纤维蛋白原、手术持续时间、手术引流比较,差异有统计学意义(P<0.05)。logistic回归分析显示,患者BMI(OR=1.231,95%CI:1.049~1.445,P=0.011)是骨科手术部位感染的独立危险因素。BMI作为手术部位感染的独立危险因素,其临界值为25.37 kg/m2。结论骨科手术患者中,肥胖患者更易发生手术部位感染,建议进一步强化肥胖患者的围手术期管理,以减少手术部位感染的发生。