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Objectives: The objectives of this paper is to conduct a bibliometric analysis to examine the research status and development trend of anterior cruciate ligament injury and reconstruction in children and adolescents over the past 20 years. Design: Descriptive Research. Methods: This study obtained information regarding studies on Anterior Cruciate Ligament Reconstruction in Children and Adolescents from the Web of Science Core Collection database. Visual and bibliometric analysis were conducted using VOSviewer, Origin 2022, Pajek64 5.18and Excel 2019. These analytic tools facilitated the analysis of various aspects, including countries/regions, institutions, authors, journals and keywords related to the research. Results: From 2003 to 2023, a total of 1328 articles were retrieved in WOS, and 637 articles were selected by two authors. The most productive institutions are Childrens Hosp Philadelphia, Kocher, ms. Their articles have the highest number of publications and citations. The American journal of sports medicine is the most frequently cited journal for articles on anterior cruciate ligament reconstruction in children and adolescents. The most common keywords used in these articles were "anterior cruciate ligament reconstruction", "injury, children, adolescent", and "skeletally immature patients". Conclusions: This study provides valuable insights into the research focus of anterior cruciate ligament reconstruction in children and adolescents. In recent years, there has been significant attention paid to areas of "the return to sport, re-repture rate and functional recovery after anterior cruciate ligament reconstruction" in this specific population. These aspects have emerged as key directions for future research in this field. [ABSTRACT FROM AUTHOR]

Prostate cancer is the most common cancer after non-melanoma skin cancer and the second leading cause of cancer deaths in US men. Its incidence and mortality rates vary substantially across geographical regions and over time, with large disparities by race, geographic regions (i.e., Appalachia), among others. The widely used Cox proportional hazards model is usually not applicable in such scenarios owing to the violation of the proportional hazards assumption. In this paper, we fit Bayesian accelerated failure time models for the analysis of prostate cancer survival and take dependent spatial structures and temporal information into account by incorporating random effects with multivariate conditional autoregressive priors. In particular, we relax the proportional hazards assumption, consider flexible frailty structures in space and time, and also explore strategies for handling the temporal variable. The parameter estimation and inference are based on a Monte Carlo Markov chain technique under a Bayesian framework. The deviance information criterion is used to check goodness of fit and to select the best candidate model. Extensive simulations are performed to examine and compare the performances of models in different contexts. Finally, we illustrate our approach by using the 2004-2014 Pennsylvania Prostate Cancer Registry data to explore spatial-temporal heterogeneity in overall survival and identify significant risk factors. [ABSTRACT FROM AUTHOR]

Background: Recruiting large cohorts efficiently can speed the translation of findings into care across a range of scientific disciplines and medical specialties. Recruitment can be hampered by factors such as financial barriers, logistical concerns, and lack of resources for patients and clinicians. These and other challenges can lead to underrepresentation in groups such as rural residents and racial and ethnic minorities. Here we discuss the implementation of various recruitment strategies for enrolling participants into a large, prospective cohort study, assessing the need for adaptations and making them in real-time, while maintaining high adherence to the protocol and high participant satisfaction. Methods: While conducting a large, prospective trial of a multi-cancer early detection blood test at Geisinger, an integrated health system in central Pennsylvania, we monitored recruitment progress, adherence to the protocol, and participants' satisfaction. Tracking mechanisms such as paper records, electronic health records, research databases, dashboards, and electronic files were utilized to measure each outcome. We then reviewed study procedures and timelines to list the implementation strategies that were used to address barriers to recruitment, protocol adherence and participant satisfaction. Results: Adaptations to methods that contributed to achieving the enrollment goal included offering multiple recruitment options, adopting group consenting, improving visit convenience, increasing the use of electronic capture and the tracking of data and source documents, staffing optimization via leveraging resources external to the study team when appropriate, and integrating the disclosure of study results into routine clinical care without adding unfunded work for clinicians. We maintained high protocol adherence and positive participant experience as exhibited by a very low rate of protocol deviations and participant complaints. Conclusion: Recruiting rapidly for large studies – and thereby facilitating clinical translation – requires a nimble, creative approach that marshals available resources and changes course according to data. Planning a rigorous assessment of a study's implementation outcomes prior to study recruitment can further ground study adaptations and facilitate translation into practice. This can be accomplished by proactively and continuously assessing and revising implementation strategies. [ABSTRACT FROM AUTHOR]

The main challenge in addressing constrained multi-objective optimization problems (CMOPs) lies in achieving a balance among convergence, diversity, and feasibility. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm with Pareto estimation via neural network named CMOEA-PeNN. In order to exploit and explore the decision space, the proposed algorithm employs a dual-population mechanism, which is trained with a self-organizing map (SOM). Firstly, the population distribution structure in decision space is mapped to objective space while preserving neighborhood information, and then the neuron weight is utilized to estimate the Pareto front (PF). Secondly, a novel approach is devised to preserve the feasibility of the population and enhance the estimation of the Pareto front by SOM. The achievement scalarizing function (ASF) is employed to choose promising solutions. This strategy could guide the population toward the optimal solution while exploring the small feasible regions. Finally, the performance of CMOEA-PeNN is compared with five state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) on three widely used benchmark problems and a real-world problem. The experimental results show that CMOEA-PeNN could archive competitive performance in solving CMOPs. [ABSTRACT FROM AUTHOR]