Notes

Evaluating local community knowledge, perspective as well as practices to improve connection way of Malaria Eradication Demonstration Venture in Mandla.
The training strategy also incorporates a "train the trainer" approach to enable CBs who have successfully completed training to train new staff or faculty.
The Clinical and Translational Science Awards (CTSA) Consortium, about 60 National Institutes of Health (NIH)-supported CTSA hubs at academic health care institutions nationwide, is charged with improving the clinical and translational research enterprise. Together with the NIH National Center for Advancing Translational Sciences (NCATS), the Consortium implemented Common Metrics and a shared performance improvement framework.

Initial implementation across hubs was assessed using quantitative and qualitative methods over a 19-month period. The primary outcome was implementation of three Common Metrics and the performance improvement framework. Challenges and facilitators were elicited.

Among 59 hubs with data, all began implementing Common Metrics, but about one-third had completed all activities for three metrics within the study period. The vast majority of hubs computed metric results and undertook activities to understand performance. Differences in completion appeared in developing and carrying outanizations proved feasible but required substantial time and resources. Considerable heterogeneity across hubs in data systems, existing processes and personnel, organizational structures, and local priorities of home institutions created disparate experiences across hubs. Future metric-based performance management initiatives across heterogeneous local contexts should anticipate and account for these types of differences.
Evaluating clinical and translational research (CTR) mentored training programs is challenging because no two programs are alike. Careful selection of appropriate metrics is required to make valid comparisons between individuals and between programs. The KL2 program provides mentored-training for early-stage CTR investigators. Clinical and Translational Awards across the country have unique KL2 programs. The evaluation of KL2 programs has begun to incorporate bibliometrics to measure KL2 scholar and program impact.

This study investigated demographic differences in bibliometric performance and post-K award funding of KL2 scholars and compared the bibliometric performance and post-K award federal funding of KL2 scholars and other mentored-K awardees at the same institution. Data for this study included SciVal and iCite bibliometrics and National Institutions of Health RePORTER grant information for mentored-K awardees (K08, K23, and KL2) at Case Western Reserve University between 2005 and 2013.

Results sstablished.
The University of Wisconsin Institute for Clinical and Translational Research hub supports multiple pilot award programs that engage cross-disciplinary Translational Teams. To support those teams, our Team Science group aims to offer a learning experience that is accessible, active, and actionable. We identified Collaboration Planning as a high-impact intervention to stimulate team-building activities that provide Translational Team members with the skills to lead and participate in high-impact teams.

We adapted the published materials on Collaboration Planning to develop a 90-minute facilitated intervention with questions in 10 areas, presuming no previous knowledge of Science of Team Science (SciTS) or team-science best practices. Attendees received a short follow-up survey and submitted a written collaboration plan with their first quarterly progress report.

Thirty-nine participants from 13 pilot teams from a wide range of disciplines engaged in these sessions. We found that teams struggled to know who to invite, that some of our questions were confusing and too grounded in the language of SciTS, and groups lacked plans for managing their information and communications. We identified several areas for improvement including ensuring that the process is flexible to meet the needs of different teams, continuing to evolve the questions so they resonate with teams, and the need to provide resources for areas where teams needed additional guidance, including information and data management, authorship policies, and conflict management.

With further development and testing, Collaboration Planning has the potential to support Translational Teams in developing strong team dynamics and team functioning.
With further development and testing, Collaboration Planning has the potential to support Translational Teams in developing strong team dynamics and team functioning.The critical processes driving successful research translation remain understudied. We describe a mixed-method case study protocol for analyzing translational research that has led to the successful development and implementation of innovative health interventions. An overarching goal of these case studies is to describe systematically the chain of events between basic, fundamental scientific discoveries and the adoption of evidence-based health applications, including description of varied, long-term impacts. The case study approach isolates many of the key factors that enable the successful translation of research into practice and provides compelling evidence connecting the intervention to measurable changes in health and medical practice, public health outcomes, and other broader societal impacts. The goal of disseminating this protocol is to systematize a rigorous approach, which can enhance reproducibility, promote the development of a large collection of comparable studies, and enable cross-case analyses. This approach, an application of the "science of translational science," will lead to a better understanding of key research process markers, timelines, and potential points of leverage for intervention that may help facilitate decisions, processes, and policies to speed the sustainable translational process. Case studies are effective communication vehicles to demonstrate both accountability and the impacts of the public's investment in research.Machine learning (ML) provides the ability to examine massive datasets and uncover patterns within data without relying on a priori assumptions such as specific variable associations, linearity in relationships, or prespecified statistical interactions. However, the application of ML to healthcare data has been met with mixed results, especially when using administrative datasets such as the electronic health record. The black box nature of many ML algorithms contributes to an erroneous assumption that these algorithms can overcome major data issues inherent in large administrative healthcare data. As with other research endeavors, good data and analytic design is crucial to ML-based studies. In this paper, we will provide an overview of common misconceptions for ML, the corresponding truths, and suggestions for incorporating these methods into healthcare research while maintaining a sound study design.The pervasive problem of irreproducibility of preclinical research represents a substantial threat to the translation of CTSA-generated health interventions. Key stakeholders in the research process have proposed solutions to this challenge to encourage research practices that improve reproducibility. However, these proposals have had minimal impact, because they either 1. take place too late in the research process, 2. focus exclusively on the products of research instead of the processes of research, and/or 3. fail to take into account the driving incentives in the research enterprise. Because so much clinical and translational science is team-based, CTSA hubs have a unique opportunity to leverage Science of Team Science research to implement and support innovative, evidence-based, team-focused, reproducibility-enhancing activities at a project's start, and across its evolution. Here, we describe the impact of irreproducibility on clinical and translational science, review its origins, and then describe stakeholders' efforts to impact reproducibility, and why those efforts may not have the desired effect. Based on team-science best practices and principles of scientific integrity, we then propose ways for Translational Teams to build reproducible behaviors. We end with suggestions for how CTSAs can leverage team-based best practices and identify observable behaviors that indicate a culture of reproducible research.
Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health (mHealth) and wearable technology. find more Data from Biometric Monitoring Technologies (BioMeTs), including mHealth and wearables, can be transformed into
that act as indicators of health outcomes and can be used to diagnose and monitor a number of chronic diseases and conditions. There are many challenges faced by digital biomarker development, including a lack of regulatory oversight, limited funding opportunities, general mistrust of sharing personal data, and a shortage of open-source data and code. Further, the process of transforming data into digital biomarkers is computationally expensive, and standards and validation methods in digital biomarker research are lacking.

In order to provide a collaborative, standardized space for digital biomarker research and validation, we present the first comprehensive, open-source software platform for end-to-end digital biomarker development
.

Here, we detail the general DBDP framework as well as three robust modules within the DBDP that have been developed for specific digital biomarker discovery use cases.

The clear need for such a platform will accelerate the DBDP's adoption as the industry standard for digital biomarker development and will support its role as the epicenter of digital biomarker collaboration and exploration.
The clear need for such a platform will accelerate the DBDP's adoption as the industry standard for digital biomarker development and will support its role as the epicenter of digital biomarker collaboration and exploration.
Access to qualified biostatisticians to provide input on research design and statistical considerations is critical for high-quality clinical and translational research. At diverse health science institutions, like the University of Michigan (U-M), biostatistical collaborators are scattered across the campus. This model can isolate applied statisticians, analysts, and epidemiologists from each other, which may negatively affect their career development and job satisfaction, and inhibits access to optimal biostatistical support for researchers. Furthermore, in the era of modern, complex translational research, it is imperative to elevate biostatistical expertise by offering innovative training.

The Michigan Institute for Clinical and Health Research established an Applied Biostatistical Sciences (ABS) network that is a campus-wide community of staff and faculty statisticians, epidemiologists, data scientists, and researchers, with the intention of supporting both researchers and biostatisticians, while protion with any network of professionals with common interests across different disciplines and professional fields regardless of size.
In clinical and translational research, data science is often and fortuitously integrated with data collection. This contrasts to the typical position of data scientists in other settings, where they are isolated from data collectors. Because of this, effective use of data science techniques to resolve translational questions requires innovation in the organization and management of these data.

We propose an operational framework that respects this important difference in how research teams are organized. To maximize the accuracy and speed of the clinical and translational data science enterprise under this framework, we define a set of eight best practices for data management.

In our own work at the University of Rochester, we have strived to utilize these practices in a customized version of the open source LabKey platform for integrated data management and collaboration. We have applied this platform to cohorts that longitudinally track multidomain data from over 3000 subjects.

We argue that this has made analytical datasets more readily available and lowered the bar to interdisciplinary collaboration, enabling a team-based data science that is unique to the clinical and translational setting.
Homepage: https://www.selleckchem.com/products/ml-si3.html