BMC Medical Research Methodology最新文献

{"title":"What actually happens in partnered health research? A concordance analysis of agreement on partnership practices in funded Canadian projects between academic and knowledge user investigators.","authors":"Kathryn M Sibley, Leah K Crockett, Brenda Tittlemier, Ian D Graham","doi":"10.1186/s12874-025-02679-y","DOIUrl":"10.1186/s12874-025-02679-y","url":null,"abstract":"<p><strong>Background: </strong>Collaborations involving partnerships between academic researchers and knowledge users can improve the relevance and potential adoption of evidence in health care practices and decision-making. However, descriptions of partnering practice characteristics are often limited to self-report from the lead academic researcher, with no comparison among team members. The primary objective of this study was to determine the extent to which nominated principal investigator (NPI) respondents of a questionnaire about funded Canadian partnered health research projects agreed with other team researchers and knowledge users (KU) on partnership practices.</p><p><strong>Methods: </strong>We conducted secondary analysis of a subset of data from 106 respondents from 53 partnered Canadian health research projects funded between 2011 and 2019. We organized projects into NPI-researcher and NPI-KU dyads, and analyzed 23 binary variables about types of knowledge users involved and approaches for involving knowledge users in the project. We calculated Kappa scores and examined if agreement varied by dyad type and time across three blocks of years of project funding using a two-way ANOVA. We also explored how agreement varied by question type (independent t-test) and by variable (Pearson Chi-Square).</p><p><strong>Results: </strong>Overall agreement on partnership practices was minimal (mean Kappa = 0.38, SD 0.27). NPI- researcher dyads had higher Kappa scores than NPI-KU dyads (p = 0.03). There were no significant differences across funding year blocks (p > 0.05). Agreement on the types of knowledge users engaged in the project was weak (mean Kappa = 0.43, SD 0.32), and there was no difference by dyad type. Agreement was minimal on the approaches for involving knowledge users the project (mean Kappa = 0.28, SD 0.31), and NPI-researcher dyads had significantly higher Kappa scores than NPI-KU dyads (p = 0.03). Variable-level agreement ranged between 47 and 98%.</p><p><strong>Conclusions: </strong>The overall low level of agreement among team members responding about the same project has implications for the continued study and practice of partnered health research. These findings highlight the caution that must be used in interpreting retrospectively assessed self-report practices. Moving forward, prospective documentation of partnered research practices offers the greatest potential to overcome the limitations of recall-based retrospective analyses.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"229"},"PeriodicalIF":3.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12502337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random-effects meta-analysis models for pooling rare events data: a comparison between frequentist and bayesian methods.","authors":"Minghong Yao, Ke Deng, Yuning Wang, Fan Mei, Kang Zou, Ling Li, Xin Sun","doi":"10.1186/s12874-025-02664-5","DOIUrl":"10.1186/s12874-025-02664-5","url":null,"abstract":"<p><strong>Background: </strong>Standard random-effects meta-analysis models for rare events exhibit significant limitations, particularly when synthesizing studies with double-zero events. While methodological advances in both frequentist and Bayesian frameworks now offer robust alternatives that bypass continuity corrections, the comparative performance of these approaches-especially between Bayesian and frequentist paradigms-remains understudied.</p><p><strong>Methods: </strong>This study evaluates the performance of ten widely used meta-analysis models for binary outcomes, using the odds ratio as the effect measure. The evaluated models comprise seven frequentist and three Bayesian approaches. Simulations systematically varied key parameters, including control event rates, treatment effects, study numbers, and heterogeneity levels, to compare model performance across four metrics: percentage bias, 95% confidence/credible interval width, root mean square error, and coverage. The methods were further illustrated through applications to two published rare events meta-analyses.</p><p><strong>Results: </strong>The results show that the beta-binomial model proposed by Kuss generally performed well, while the generalised estimating equations did not. In cases where heterogeneity is not large, all models tended to have a good performance except for the generalised estimating equations. When the heterogeneity is large, none of the compared models produced good performance. The Bayesian model incorporating the Beta-Hyperprior proposed by Hong et al. performed well, followed by the binomial-normal hierarchical model proposed by Bhaumik.</p><p><strong>Conclusions: </strong>In summary, the beta-binomial model proposed by Kuss is recommended for rare events meta-analyses, and the Bayesian model is a promising method for pooling rare events data.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"228"},"PeriodicalIF":3.4,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145211616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the performance of different machine learning algorithms based on SMOTE in predicting musculoskeletal disorders in elementary school students.","authors":"Sara Manoochehri, Maryam Zamani, Maryam Afshari, Ali Reza Soltanian, Zohreh Manoochehri","doi":"10.1186/s12874-025-02654-7","DOIUrl":"10.1186/s12874-025-02654-7","url":null,"abstract":"","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"227"},"PeriodicalIF":3.4,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145211598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PAM clustering algorithm based on mutual information matrix for ATR-FTIR spectral feature selection and disease diagnosis.","authors":"Francesca Condino, Maria Caterina Crocco, Rita Guzzi","doi":"10.1186/s12874-025-02667-2","DOIUrl":"10.1186/s12874-025-02667-2","url":null,"abstract":"<p><p>The ATR-FTIR spectral data represent a valuable source of information in a wide range of pathologies, including neurological disorders, and can be used for disease discrimination. To this end, the identification of the potential spectral biomarkers among all possible candidates is needed, but the amount of information characterizing the spectral dataset and the presence of redundancy among data could make the selection of the more informative features cumbersome. Here, a novel approach is proposed to perform feature selection based on redundant information among spectral data. In particular, we consider the Partition Around Medoids algorithm based on a dissimilarity matrix obtained from mutual information measure, in order to obtain groups of variables (wavenumbers) having similar patterns of pairwise dependence. Indeed, an advantage of this grouping algorithm with respect to other more widely used clustering methods, is to facilitate the interpretation of results, since the centre of each cluster, the so-called medoid, corresponds to an observed data point. As a consequence, the obtained medoid can be considered as representative of the whole wavenumbers belonging to the same cluster and retained in the subsequent statistical methods for disease prediction. An application on real data is finally reported to show the ability of the proposed approach in discriminating between patients affected by multiple sclerosis and healthy subjects.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"225"},"PeriodicalIF":3.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12487623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How long does it take to complete and publish a systematic review of animal studies?","authors":"Julia Victoria Bugajska, Bernard Friedrich Hild, David Brüschweiler, Enrico Daniele Meier, Alexandra Bannach-Brown, Kimberley Elaine Wever, Benjamin Victor Ineichen","doi":"10.1186/s12874-025-02672-5","DOIUrl":"10.1186/s12874-025-02672-5","url":null,"abstract":"<p><strong>Introduction: </strong>Conducting a rigorous systematic review of animal studies requires a priori registration of a study protocol. However, it remains unknown how many of these registered studies culminate in publication and how long it takes to complete such a systematic review. Thus, this study had two objectives: (1) to assess the proportion of registered protocols that result in publication, and (2) to determine the time required to complete and publish systematic reviews of animal studies after protocol registration.</p><p><strong>Methods: </strong>All available systematic reviews protocols of animal study were manually downloaded from PROSPERO, the international registry of systematic review protocols. Start and completion date as well as topical and demographic data were extracted, complemented by a web-scraping approach. Assessment of publication status was achieved through a systematic literature search.</p><p><strong>Results: </strong>From a total of 1,771 protocols, 406 were excluded due to recent start dates. This left 1,365 protocols eligible for the final analysis. Among these, 694 (51%) resulted in a published systematic review. Median time to complete and publish a systematic review was 11.5 months (range: 0.13-44.9 months) and 16.2 months (range: 1.0-49.7 months), respectively. This time was 69% more until submission than anticipated by the authors (6.8 months [range: 0.9-48.0]).</p><p><strong>Conclusion: </strong>Only half of registered protocols resulted in publication, suggesting possible publication bias. Authors can expect to complete and publish an animal systematic review within approximately one year.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"226"},"PeriodicalIF":3.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12487012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BRIDGe recruitment strategies for frail older adults in intervention trials: lessons learned from the ACTIVE-AGE@home trial.","authors":"Jade Tambeur, Emma De Keyser, Elke De Smedt, Dimitri Vrancken, Lieven Annemans, David Beckwée, Wim Peersman, Siddhartha Lieten, Dominique Van de Velde, Patricia De Vriendt","doi":"10.1186/s12874-025-02657-4","DOIUrl":"10.1186/s12874-025-02657-4","url":null,"abstract":"","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"224"},"PeriodicalIF":3.4,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and evaluation of methods of clinical utility-based cut-point selection of diagnostic biomarkers: an analysis based on population-level parametric distributions of test results with application of clinical diagnostic data.","authors":"Mojtaba Hassanzad, Karimollah Hajian-Tilaki, Zinatossadat Bouzari","doi":"10.1186/s12874-025-02656-5","DOIUrl":"10.1186/s12874-025-02656-5","url":null,"abstract":"<p><strong>Introduction: </strong>The cut-point selection of biomarkers based on clinical benefit of test results rather than accuracy-based is of interest for decision makers. We adapted the four methods of cut-point selection based on clinical utility of test results including Youden, Product, Union and the absolute difference of total utility with 2 times of AUC.</p><p><strong>Methods: </strong>The population-based parametric pairs of distributions of test results comprising homoscedastic binormal model, non-homoscedastic binormal, bigamma and biexponential included in the study. For each pair of distributions for diseased and non-diseased the utility-based metrics of cut-point were calculated under different degrees of AUC and prevalence. The prevalence was varied from 0.01 to 0.05, 0.10, 0.30, and 0.50.</p><p><strong>Results: </strong>For a low prevalence as low as 0.01, the two methods of Product, and Union that maximize and minimize the related metrics respectively yield rather similar a true value of cut-point but the Youden-based utility metrics suggest rather similarly the true value of for an optimal cut-point. In opposition, the Youden-based utility metric and the absolute difference of total utility with 2 times of AUC produce extremely high value for optimal cut-point because of their s-shaped metrics over various cut-off values. As prevalence increases to 10% or more, the metric of Youden -based utility becomes concave and its cut-point becomes closer to other methods. The four proposed methods yield roughly identical cut-point at prevalence of 10% or more for high accuracy of 0.90. The greater discrepancy of optimal cut-point was shown in skew distributions of bigamma and biexponential with low prevalence and low AUC. For prevalence < 10%, the utility-based produces larger cut-point than accuracy-based methods in our clinical data for CRP. The methods of utility-based cut-point selection were explained by CRP in predicting preeclampsia, and other clinical data.</p><p><strong>Conclusion: </strong>The inconsistency of optimal cut-points is possible by different methods of utility-based criteria depending on the prevalence and degree of AUC. For high AUC, and prevalence > 10%, the four proposed methods yield rather identical optimal cut-points. Further studies of simulation are needed to evaluate the bias and sampling variability of utility-based of cut-point selection.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"223"},"PeriodicalIF":3.4,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing causal inference methods for point exposures with missing confounders: a simulation study.","authors":"Luke Benz, Alexander W Levis, Sebastien Haneuse","doi":"10.1186/s12874-025-02675-2","DOIUrl":"10.1186/s12874-025-02675-2","url":null,"abstract":"<p><p>Causal inference methods based on electronic health record (EHR) databases must simultaneously handle confounding and missing data. In practice, when faced with partially missing confounders, analysts may proceed by first imputing missing data and subsequently using outcome regression or inverse-probability weighting (IPW) to address confounding. However, little is known about the theoretical performance of such reasonable, but ad hoc methods. Though vast literature exists on each of these two challenges separately, relatively few works attempt to address missing data and confounding in a formal manner simultaneously. In a recent paper Levis et al. (Can J Stat e11832, 2024) outlined a robust framework for tackling these problems together under certain identifying conditions, and introduced a pair of estimators for the average treatment effect (ATE), one of which is non-parametric efficient. In this work we present a series of simulations, motivated by a published EHR based study (Arterburn et al., Ann Surg 274:e1269-e1276, 2020) of the long-term effects of bariatric surgery on weight outcomes, to investigate these new estimators and compare them to existing ad hoc methods. While methods based on ad hoc combinations of imputation and confounding adjustment perform well in certain scenarios, no single estimator is uniformly best. We conclude with recommendations for good practice in the face of partially missing confounders.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"222"},"PeriodicalIF":3.4,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145190983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling strategies for a flexible estimation of the crude cumulative incidence in the context of long follow-ups: model choice and predictive ability evaluation.","authors":"Giacomo Biganzoli, Giuseppe Marano, Patrizia Boracchi","doi":"10.1186/s12874-025-02650-x","DOIUrl":"10.1186/s12874-025-02650-x","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Advancements in treatments for chronic diseases, such as breast cancer, have expanded our ability to observe patient outcomes beyond disease-related mortality, including events like distant recurrences. However, competing events can complicate the interpretation of primary outcomes, making the crude cumulative incidence function the most reliable measure for accurate follow-up analysis. Long-term studies require flexible modeling to accommodate intricate, time-dependent effects and interactions among covariates. Traditional models, such as the proportional sub-distribution hazards model, often insufficient to address these complexities. Although more adaptable methods have been proposed, there is still a need to systematically assess model complexity, particularly for exploratory purposes. This article presents a statistical learning workflow designed to evaluate model complexity in crude cumulative incidence and introduces a time-dependent metric for predictive accuracy. This framework provides researchers with an enhanced toolkit for robustly addressing the complexities of long-term outcome modeling and deriving interpretable prognostic algorithms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We demonstrate our approach using data on time-to-distant breast cancer recurrences from the Milan 1 and Milan 3 trials, which have extensive follow-up periods. Two flexible modeling frameworks-pseudo-observations and sub-distribution hazard models-are employed, enhanced with spline functions to capture baseline hazard and risk. Our proposed workflow integrates graphical representations of Aalen-Johansen estimates for crude cumulative incidence, enabling researchers to visually hypothesize and adjust model complexity to match the studied phenomenon. Information criteria guide model selection to approximate the underlying data structure. Using bootstrapped data perturbations and time-dependent predictive accuracy measures, adjusted with Harrell's optimism correction, we identify the optimal model structure, balancing explainability, predictivity, and generalizability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our findings highlight the importance of data perturbation and validation through optimism-corrected predictive measures following the original data analysis. The initial model structure may differ from the most robust model identified through iterative perturbation. The ideal model has high robustness (most frequently selected in perturbations), strong explainability, and predictive capacity. When perturbation results are inconsistent, evaluating various time-dependent predictive measures offers additional insights, particularly regarding the trade-off between model complexity and predictive gains. In cases where predictive improvement is minimal, simpler and more explainable model structures are preferable.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The proposed statistical learning workflow, informed by domain expertise, allows for incorporating c","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"217"},"PeriodicalIF":3.4,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145190972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the causal effects of exposure mixtures: a generalized propensity score method.","authors":"Qian Gao, Ting Li, Guiming Zhu, Juping Wang, Kexin Qiu, Liangpo Liu, Xiujuan Yang, Tong Wang","doi":"10.1186/s12874-025-02673-4","DOIUrl":"10.1186/s12874-025-02673-4","url":null,"abstract":"<p><strong>Background: </strong>In environmental epidemiology and many other fields, estimating the causal effects of multiple concurrent exposures holds great promise for driving public health interventions and policy changes. Given the predominant reliance on observational data, confounding remains a key consideration, and generalized propensity score (GPS) methods are widely used as causal models to control measured confounders. However, current GPS methods for multiple continuous exposures remain scarce.</p><p><strong>Methods: </strong>We proposed a novel causal model for exposure mixtures, called nonparametric multivariate covariate balancing generalized propensity score (npmvCBGPS). A simulation study examined whether npmvCBGPS, an existing multivariate GPS (mvGPS) method, and a linear regression model for the outcome can accurately and precisely estimate the effects of exposure mixtures in a variety of common scenarios. An application study illustrated the analysis of the causal role of per- and polyfluoroalkyl substances (PFASs) on BMI.</p><p><strong>Results: </strong>The npmvCBGPS achieved acceptable covariate balance in all scenarios. The estimates were close to the true value as long as either the exposure or the outcome model was correctly specified, and the results were less impacted by correlations among mixture components. The accuracy and precision of mvGPS and the linear regression model relied on the correctly specified exposure model and outcome model, respectively. The npmvCBGPS outperformed mvGPS in all scenarios. The npmvCBGPS achieved better covariate balance than mvGPS and provided an overall inverse trend between the PFAS mixtures with BMI.</p><p><strong>Conclusions: </strong>In this study, we proposed npmvCBGPS to accurately estimate the causal effects of multiple exposure mixtures on health outcomes. Our approach is applicable across various domains, with a particular emphasis on environmental epidemiology.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"221"},"PeriodicalIF":3.4,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}