Clinical Trials最新文献

{"title":"Implementation challenges in a community-based randomized clinical trial in the rural settings of India.","authors":"Ruchi Vaidya, Nayanjeet Chaudhury, Sneha Sewa, Suman V","doi":"10.1177/17407745261488336","DOIUrl":"https://doi.org/10.1177/17407745261488336","url":null,"abstract":"<p><strong>Background: </strong>Community-based randomized controlled trials are the benchmark for evaluating public health interventions due to their real-world relevance. However, their implementation, particularly in resource-limited, rural settings, is challenging. This article details our experience in addressing these hurdles during a nutrition intervention study in rural Karnataka, India.</p><p><strong>Methods: </strong>Our study was a descriptive analysis of the implementation challenges encountered as part of a larger cluster randomized clinical trial among adults with prediabetes residing in rural Karnataka. We analysed challenges encountered during community mobilization, screening, recruitment, data collection, and intervention delivery. A team of six research fellows was rooted in the communities, and a qualitative assurance supervisor conducted weekly field visits to identify and address challenges. In-depth qualitative interviews were conducted with the research fellows, and data were analysed using a thematic content analysis. The study's problem-solving framework was based on agile management principles.</p><p><strong>Results: </strong>We encountered significant challenges related to community mistrust, patriarchal sociocultural barriers, and logistical and operational hurdles. The research team addressed these problems in real time by living within the communities, leveraging the established trust of local health workers, and implementing an agile problem-solving framework. The study's focus on cultural sensitivity and on-the-ground problem-solving was crucial to its high participant retention rate.</p><p><strong>Conclusion: </strong>Our findings highlight the critical methodological and ethical challenges inherent in community-based randomized controlled trials and demonstrate that a flexible, adaptive, and culturally sensitive approach is essential for success in similar settings. The innovative, real-time solutions we developed can be a valuable guide for other community-academic partnerships, providing insights that bridge the gap between scientific protocol and real-world implementation.</p><p><strong>Trial registration number: </strong>The trial is registered in the clinical trial registry of India (CTRI/2023/03/050421).</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261488336"},"PeriodicalIF":2.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated surveillance of primary outcome reporting in clinical trials: Development and validation of a scalable screening pipeline.","authors":"Hayden Farquhar","doi":"10.1177/17407745261491903","DOIUrl":"https://doi.org/10.1177/17407745261491903","url":null,"abstract":"<p><strong>Background: </strong>Detecting discrepancies between registered and published trial outcomes currently requires manual expert review, limiting surveillance to small samples. No scalable automated system exists for continuous monitoring of outcome reporting integrity across the clinical trial literature. We aimed to develop and validate an automated screening pipeline for primary outcome discrepancies and to characterise its performance at scale.</p><p><strong>Methods: </strong>We developed an automated pipeline that retrieves trial registrations from ClinicalTrials.gov, matches them to publications via PubMed and screens for primary outcome discrepancies using a term-matching algorithm. We applied the pipeline to all 46,365 completed interventional trials with posted results (2015-2025) and validated performance against manual expert review of 123 trial-publication pairs, with independent large language model re-review as a second rater.</p><p><strong>Results: </strong>The pipeline processed 46,365 trials in under 48 h, matching 27,555 (59.4%) to publications and confirming 13,595 pairs via trial identifier verification. Automated screening classified 10,231 (75.3%) as concordant and flagged 3353 (24.7%) for review. Against manual review of 123 pairs, the screen achieved 100% specificity and 16.7% sensitivity (1 of 6 discrepancies); the flag's positive predictive value could not be estimated because the validation sample contained only one flagged pair. Independent large language model re-review showed 99.2% agreement (Cohen's kappa = 0.91). Manual review estimated a true discrepancy rate of 4.9% (95% confidence interval = 1.6%-8.9%), predominantly minor modifications (timeframe extensions, operationalisation changes) rather than substantive outcome switching. Automated flag rates were not associated with primary outcome statistical significance (21.0% vs 20.6%; <i>p</i> = 0.745).</p><p><strong>Discussion: </strong>Automated screening can efficiently triage tens of thousands of trials for outcome reporting surveillance, deprioritising 75% of pairs as concordant and concentrating manual review on the flagged subset. The pipeline provides a foundation for continuous, near-real-time monitoring of outcome reporting integrity that could be integrated into journal peer review or regulatory workflows.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261491903"},"PeriodicalIF":2.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Audit and feedback interventions for recruitment and retention in clinical trials: A systematic review.","authors":"Mojca Cerar, Taylor Coffey, Beatriz Goulão, Katie Gillies","doi":"10.1177/17407745261488366","DOIUrl":"https://doi.org/10.1177/17407745261488366","url":null,"abstract":"<p><strong>Background/aims: </strong>This study had two objectives: (1) to determine the effectiveness of audit and feedback interventions for improving recruitment and retention in randomised controlled trials (hereinafter referred to as trials) and (2) to identify and code behaviour change techniques in the reported audit and feedback interventions.</p><p><strong>Methods: </strong>A systematic review was conducted to identify the effect of audit and feedback intervention (alone or in combination) on participant recruitment and/or retention compared to other strategies. Audit and feedback targeting clinical practice was excluded. Embase and MEDLINE databases were searched up to 9 January 2026. Interventions were coded using Behaviour Change Technique Taxonomy version 1. The results are presented in a narrative form.</p><p><strong>Results: </strong>Four studies met the eligibility criteria: three randomised and one non-randomised evaluation. Due to the heterogeneity of interventions and outcomes, a meta-analysis was deemed inappropriate. None of the randomised evaluations demonstrated a statistically significant effect of audit and feedback on recruitment outcomes; the non-randomised study reported an increasing trend in recruitment. The risk of bias was low for one and unclear for two trials. The observational study was at a critical risk of bias. None of the studies applied behavior change techniques explicitly. Twenty-one behavior change techniques were identified at least once across both audit and feedback and co-interventions. Ten behavior change techniques were identified in audit and feedback interventions only (mean = 4.8, standard deviation = 2.4). The behavior change technique identified most often in audit and feedback interventions was '2.7. Feedback on outcome(s) of behaviour' (n = 4, 100%; e.g. emails with league tables describing recruitment performance).</p><p><strong>Conclusions: </strong>This review identified audit and feedback evaluations used to improve recruitment/ retention in a trial context and identified which behavior change techniques are currently used and their frequency. Only one study conceptualised its intervention as audit and feedback and utilised existing audit and feedback literature. Our analysis and behavior change technique coding were restricted to the limited reporting of intervention designs. Further research is needed to understand how audit and feedback is currently being used and whether it can be effective in improving recruitment and retention in trials.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261488366"},"PeriodicalIF":2.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transparency of clinical trials: Reproducing and extending analyses.","authors":"Peter Doshi, Mark Jones, Kaiser Fung, Linda Wastila","doi":"10.1177/17407745261491900","DOIUrl":"https://doi.org/10.1177/17407745261491900","url":null,"abstract":"<p><strong>Background: </strong>Between 2021 and 2023, deidentified patient-level data and associated programming code from the biologics license application for Pfizer's covid-19 vaccine were published on the Internet. The release, which appears to be unprecedented in permitting public access to such granular data, allows new researchers to independently reproduce published analyses and extend published findings by conducting analyses that the original investigators did not report.</p><p><strong>Methods: </strong>We downloaded 2370 files (26 gigabytes), including more than 30 SAS programming files, 2 data reviewer's guides, and 180 SAS Transport datasets from phmpt.org. We documented programming files and adapted them to run in our environment, aiming to reproduce four published tables and a figure reporting efficacy and safety results. We also sought to estimate vaccine efficacy against covid-19 hospitalization, a clinically meaningful and public health relevant measure of potential protection against serious disease that has not been reported.</p><p><strong>Results: </strong>We were able to reproduce 334 of 352 data elements from four published efficacy and safety tables. Lacking original code, we could not reproduce one key efficacy analysis (a cumulative incidence curve). For our extended analyses, we judged that vaccine efficacy against covid-19 hospitalization could not be reliably calculated using the available datasets due to a paradoxical (and therefore unreliable) finding of more covid-19 hospitalizations (n = 43) than cases of severe covid-19 (n = 31).</p><p><strong>Discussion: </strong>The public release of original datasets and patient-level data enabled the exact reproduction of most published efficacy and safety analyses, establishing a firm basis for further analyses. Our experience highlights the feasibility and enormous value of publicly releasing and analyzing patient-level clinical trial datasets.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261491900"},"PeriodicalIF":2.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149179775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evidence supporting European Medicines Agency drug approvals (2020-2023): A cross-sectional study of trial design and outcomes.","authors":"Maximilian Siebert, Laura Caquelin, Florian Naudet, Joseph S Ross, Reshma Ramachandran","doi":"10.1177/17407745261437363","DOIUrl":"10.1177/17407745261437363","url":null,"abstract":"<p><strong>Background: </strong>The strength and transparency of clinical trial evidence supporting drug approvals have become increasingly scrutinized, particularly considering the increased use of regulatory flexibility and expedited pathways. While US Food and Drug Administration standards have been extensively analyzed, evidence standards at the European Medicines Agency remain less well-characterized. Thus, this study aims to systematically assess the design, quality, and outcomes of pivotal efficacy trials supporting European Medicines Agency drug approvals for new active substances between 2020 and 2023.</p><p><strong>Methods: </strong>We conducted a cross-sectional analysis of drugs receiving positive opinions from the European Medicines Agency's Committee for Medicinal Products for Human Use and subsequent approval by the European Commission between January 2020 and December 2023. Data were extracted from European Public Assessment Reports and European Medicines Agency medicine databases. Key variables included trial design features, primary endpoint type and achievement status, and justification for approval in cases of failed efficacy endpoints.</p><p><strong>Results: </strong>Between 2020 and 2023, 227 new drugs were approved by the European Medicines Agency for 234 indications. Of these, 69 (30.4%) were granted orphan designation, and 53 (23.3%) were approved via an expedited pathway, most commonly conditional approval (29 drugs, 12.8%). Cancer was the leading therapeutic area, accounting for 59 approvals (26.0%). Approvals were supported by 404 pivotal clinical trials. Of the 227 drugs, 177 (78.0%) were supported by at least one randomized controlled trial, of which 138 (78.0%) were blinded. Out of those, 139 (78.5%) were supported exclusively by superiority trials. Regarding endpoint classification, 124/227 drugs (54.6%) relied exclusively on surrogate endpoints and 69 (30.4%) on clinical endpoints. Overall, 22 approvals (9.7%) were supported by at least one pivotal trial in which at least one primary endpoint was not met; in five of these cases (22.7%), the failed trial was the sole pivotal trial. The most common rationale for approval despite null primary results was reliance on the totality of evidence, secondary endpoints, or clinical judgment (nine products; 40.9%).</p><p><strong>Conclusion: </strong>Our findings reveal substantial variability in the design and evidentiary strength of pivotal trials supporting European Medicines Agency approvals between 2020 and 2023. While the majority of studies were randomized controlled trials, reliance on surrogate endpoints was common. That 10% of approvals were based on pivotal trials with null primary endpoints highlights the nuanced role of regulatory judgment in therapeutic evaluation. These findings prompt reflection on evolving evidence standards in drug regulation and underscore the need for transparency and consistent justifications.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"490-499"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147627376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New oncology trial designs and equipoise: Commentary on Heynemann et al.","authors":"Jonathan Kimmelman","doi":"10.1177/17407745261471455","DOIUrl":"10.1177/17407745261471455","url":null,"abstract":"<p><p>This essay responds to Heymemann et al.'s analysis of the applicability of clinical equipoise to emerging trial methods in cancer. Briefly, I argue that equipoise presses study designers to conduct trials at the point of maximum community uncertainty. Accordingly, equipoise encourages maximum information gain per patient enrolled (or inversel, minimal patient burden per gain in information). As such, equipoise is as much a condition for the ethical conduct of new study designs as it is for old ones. I close by suggesting that, whereas Heymemann et al. suggest new trial designs present a challenge to the operationalization of equipoise, I argue that equipoise presents a challenge for those wishing to operationalize new trial designs.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"469-471"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148929130","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 clinical trial hazard functions.","authors":"Daniel F Heitjan","doi":"10.1177/17407745261439661","DOIUrl":"10.1177/17407745261439661","url":null,"abstract":"<p><strong>Background: </strong>Although the analysis of event-based clinical trials commonly relies on assumptions about the underlying hazard functions, in practice it is rare to see estimates of those functions.</p><p><strong>Methods: </strong>I describe conventional and novel methods for estimating the hazard function using discrete and discretized continuous survival models. The conventional approach involves parametric modeling; the novel approach applies Bayesian model averaging to flexible modeling by splines or fractional polynomials. I evaluate the methods in a Monte Carlo study and illustrate them in the analysis of three historical clinical trials.</p><p><strong>Results: </strong>Although flexible models can capture features of the hazard functions-such as multimodality-that parametric models miss, they are not foolproof. Spline modeling was generally the most reliable, in the sense of yielding good coverage probabilities for the mean and median with modest loss of efficiency. In the examples, the discreteness of the measurements-days, weeks, or months-had little effect on the shape of estimated hazard functions. All three data sets showed some evidence of departure from the proportional hazards assumption, but in only one did a test for proportionality detect this departure.</p><p><strong>Conclusion: </strong>Flexible parametric models, estimated in the Bayesian model averaging framework, offer a robust approach to recovering the shape of the hazard function. Analyses of three clinical trial databases suggest that visualization of the hazard function can be a valuable adjunct to conventional survival analysis.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"500-508"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764920","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":"Accounting for non-adherence: A re-analysis of the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results trial.","authors":"Kathrine Kold Sørensen, Johan Sebastian Ohlendorff, Anders Munch, Puriya Daniel Würtz Yazdanfard, Mikkel Porsborg Andersen, Ulrik Pedersen-Bjergaard, Bochra Zareini, Thomas Alexander Gerds, Christian Torp-Pedersen","doi":"10.1177/17407745261449668","DOIUrl":"10.1177/17407745261449668","url":null,"abstract":"<p><strong>Background/aims: </strong>The Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results trial randomized patients with diabetes to liraglutide or placebo. The per-protocol analysis conditioned on post-baseline adherence and therefore lacked a well-defined estimand. This study sought to evaluate a subset of Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results to estimate the effect of sustained liraglutide exposure corresponding to a well-defined per-protocol analysis.</p><p><strong>Method: </strong>We used the roadmap of targeting learning to define the estimand for the sustained treatment analysis and estimated it with longitudinal targeted minimum loss-based estimation, accounting for protocol deviations and censoring using post-baseline confounders. The results were compared to the intention-to-treat analysis. We report 3.5-year risks of the primary composite outcome (myocardial infarction, stroke, or cardiovascular mortality) and secondary outcomes (each of the primary outcome's components, revascularization, unstable angina pectoris, heart failure, and all-cause mortality).</p><p><strong>Results: </strong>The intention-to-treat analysis estimated 3.5-year risks for the primary composite outcome of 11.8% (95% confidence interval: 10.8 to 12.8) in the liraglutide arm and 13.3% (95% confidence interval: 12.3 to 14.3) in the placebo arm, with a risk difference of 1.5% (95% confidence interval: 0.1 to 2.9). Accounting for post-baseline confounders, the sustained treatment analysis estimated 3.5-year risks of 11.4% (95% confidence interval: 10.4 to 12.5) for sustained exposure to liraglutide versus 12.6% (95% confidence interval: 11.5 to 13.7) for sustained exposure to placebo, yielding a risk difference of 1.1% (95% confidence interval: -0.4 to 2.6). Sustained exposure to liraglutide showed no statistically significant difference compared with sustained exposure to placebo for any secondary outcomes at 3.5 years.</p><p><strong>Conclusion: </strong>We used contemporary methods for causal inference to define and estimate the sustained treatment effects of the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results study by accounting for both baseline and time-varying confounders. The results were consistent with the findings of the original Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results trial.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"517-525"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148142920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty and novel clinical trial designs in oncology - when should equipoise matter?","authors":"Sarah Heynemann, Wendy Lipworth, Sue-Anne McLachlan, Jennifer Philip, Tom John, Ian Kerridge","doi":"10.1177/17407745261469887","DOIUrl":"10.1177/17407745261469887","url":null,"abstract":"<p><p>Equipoise is usually considered a requirement for the ethical conduct of phase III randomised controlled trials. Notwithstanding theoretical critiques since the concept's initial proposal, equipoise has remained an important moral consideration. In oncology, many research questions are now pursued via clinical trial designs that diverge in various ways from conventional, fixed-design, two-arm randomised controlled trials. In this article, we outline the challenges that these new designs pose for implementing equipoise. Specifically, we consider challenges posed by trials incorporating seamless designs, 'immortal' trials, and trials incorporating adaptive techniques. While we note that equipoise retains a central role in the ethical evaluation of conventional randomised controlled trials, we contend that its utility as a moral construct is challenged by more recent, 'novel randomised controlled trial' designs. Consequently, other ethical tools become important in considering the different types of risk that trial participants are exposed to in modern clinical trial designs.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"457-468"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148944392","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":"On the ethical framework for clinical trials.","authors":"Colin B Begg","doi":"10.1177/17407745261471442","DOIUrl":"10.1177/17407745261471442","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"455-456"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148808368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}