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Objective Law powerfully influences health and can be a critical tool for promoting population well-being. Evaluation research is needed to measure the health effects of law and guide policy making and implementation. The purpose of this study was to assess trends in National Institutes of Health (NIH) funding for scientific public health law research (PHLR). Methods Using data from the UberResearch NIH grant repository, we collected and coded all grants with a focus on health law between FY'85 and FY'14 and then analyzed the grants by funding agency and topic areas. Results Between FY'85 and FY'14, NIH funded 510 research grants on health policy making, the health effects of laws or enforcement practices. On average, 4 PHLR grants were funded annually with a median total funding of $545 956 (range: $2535-$44 052 300) and a median annual funding of $205 223 (range: $2535-$7 019 517). Conclusions National Institutes of Health has supported important PHLR but not nearly to the extent necessary to ensure that public health laws affecting the population are evaluated in a rigorous and timely manner. In addition to greater funding evaluation research, NIH can increase its support for creating legal datasets, fund training in PHLR, and work with the National Library of Medicine to create Medical Subject Headings (MeSH) terms related to PHLR.

Contains fulltext : 170418.pdf (Publisher’s version ) (Open Access) A new class of social web-based metrics for scholarly publications (altmetrics) has surfaced as a complement to traditional citation-based metrics. Our aim was to study and characterize those recent papers in the field of Parkinson's disease which had received the highest Altmetric Attention Scores and to compare this attention measure to the traditional metrics. The top 20 papers in our analysis covered a variety of topics, mainly new disease mechanisms, treatment options and risk factors for the development of PD. The main media sources for these high attention papers were news items and Twitter. The papers were published predominantly in high impact journals, suggesting a correlation between altmetrics and conventional metrics. One paper published in a relatively modest journal received a significant amount of attention, reflecting that public attention does not always parallel the traditional metrics. None of the most influential papers in PD, as reviewed by Ponce and Lozano (2011) made it to our list, suggesting that recent publications receive higher attention scores, and that altmetrics may omit older, seminal work in the field.

For the biomedical sciences, the Medical Subject Headings (MeSH) make available a rich feature which cannot currently be merged properly with widely used citing/cited data. Here, we provide methods and routines that make MeSH terms amenable to broader usage in the study of science indicators: using Web-of-Science (WoS) data, one can generate the matrix of citing versus cited documents; using PubMed/MEDLINE data, a matrix of the citing documents versus MeSH terms can be generated analogously. The two matrices can also be reorganized into a 2-mode matrix of MeSH terms versus cited references. Using the abbreviated journal names in the references, one can, for example, address the question whether MeSH terms can be used as an alternative to WoS Subject Categories for the purpose of normalizing citation data. We explore the applicability of the routines in the case of a research program about the amyloid cascade hypothesis in Alzheimer's disease (AD). One conclusion is that referenced journals provide archival structures, whereas MeSH terms indicate mainly variation (including novelty) at the research front. Furthermore, we explore the option of using the citing/cited matrix for main-path analysis as a by-product of the software., Comment: accepted for publication in Scientometrics, August 2016

The PaTH (University of Pittsburgh/UPMC, Penn State College of Medicine, Temple University Hospital, and Johns Hopkins University) clinical data research network initiative is a collaborative effort among four academic health centers in the Mid-Atlantic region. PaTH will provide robust infrastructure to conduct research, explore clinical outcomes, link with biospecimens, and improve methods for sharing and analyzing data across our diverse populations. Our disease foci are idiopathic pulmonary fibrosis, atrial fibrillation, and obesity. The four network sites have extensive experience in using data from electronic health records and have devised robust methods for patient outreach and recruitment. The network will adopt best practices by using the open-source data-sharing tool, Informatics for Integrating Biology and the Bedside (i2b2), at each site to enhance data sharing using centrally defined common data elements, and will use the Shared Health Research Information Network (SHRINE) for distributed queries across the network.

BACKGROUND Neurorehabilitation covers a large range of disorders, assessment approaches and treatment methods. There have been previous citation analyses of rehabilitation and of its subfields. However, there has never been a comprehensive citation analysis in neurorehabilitation. OBJECTIVE The present study reports findings from a citation analysis of the top 100 most cited neurorehabilitation papers to describe the research trends in the field. METHODS A de-novo keyword search of papers indexed in the Web of Science Core Collection database yielded 52,581 papers. A candidate pool of the 200 most-cited papers published between 2005 and 2016 was reviewed by the clinician authors. The papers in the top 100 deemed to be irrelevant were discarded and replaced by the most highly-cited articles in the second tier deemed to be clinically relevant. RESULTS The most frequently cited neurorehablitation papers appeared in Stroke, Movement Disorders, and Neurology. Papers tended to focus on treatments, especially for stroke. Authorship trends suggest that top cited papers result from group endeavors, with 90% of the papers involving a collaboration among 3 or more authors. CONCLUSION Treatment studies, often focused on stroke, appear to have the highest impact in the field of neurorehabilitation.

We have compiled a list of the 100 most cited researchers in Parkinson's disease since 1985 together with H-Indices as a means to assess productivity and impact. Within the total-citations ranking, "broad impact" citations are used as a way of identifying those researchers whose work is cited widely beyond the Parkinson's disease research community. Finally, we present a table of the most cited researchers this decade for a comparison of the two with analysis.

Using bibliometric data from 269 Alzheimer investigators and the 167,142 researchers contained in their two-step collaboration network (i.e., co-authors and co-authors of co-authors), an eigen decomposition of the 13,254 unique Medical Subject Heading (MeSH) terms associated with the 43,736 papers authored by the Alzheimer researchers was performed. A correspondence-analysis-based transformation of the data produced a bench-to-bedside translational spectrum along which each of the original 269 Alzheimer investigators were placed. The spectrum was found to naturally divide into two partitions one of which housed basic scientists while the other grouped together clinical researchers. In addition to the semantic partitions, two main coauthor subgroups were isolated, and the authors who were most central to those co-author subgroups were analyzed for their ability to bridge the “translational divide” which separated researchers grouped in the “bench” (i.e. basic science) partition from those in the “bedside” (i.e., clinical investigation) partition. If a given research community can be partitioned into bench and bedside components, then the possibility exists to use such a dataset to identify people who might be best suited to attempt to bridge the “translational divide” which often exists between those researchers who make scientific breakthroughs in the lab and those clinical investigators capable of bringing the bench discoveries to the patients in the form of clinical trials.

Huntington's disease (HD), research has grown dramatically over the last 25 years of research to the point where an analysis of productivity is warranted. We have compiled a list of the 100 most-cited researchers in HD together with H-Indices as a means to assess productivity and impact over the last 25 years. We also present a table of the most cited researchers of the last decade for comparison.

John P. A. Ioannidis and colleagues asked the most highly cited biomedical scientists to score their top-ten papers in six ways.

We have generated a list of highly influential biomedical researchers based on Scopus citation data from the period 1996-2011. Of the 15,153,100 author identifiers in Scopus, approximately 1% (n=149,655) have an h-index >=20. Of those, we selected 532 authors who belonged to the 400 with highest total citation count (>=25,142 citations) and/or the 400 with highest h-index (>=76). Of those, we selected the top-400 living core biomedical researchers based on a normalized score combining total citations and h-index. Another 62 authors whose focus is outside biomedicine had a normalized score that was at least as high as the score of the 400th core biomedical researcher. We provide information on the profile of these most influential authors, including the most common Medical Subject Heading terms in their articles that are also specific to their work, most common journals where they publish, number of papers with over 100 citations that they have published as first/single, last, or middle authors, and impact score adjusted for authorship positions, given that crude citation indices and authorship positions are almost totally orthogonal. We also show for each researcher the distribution of their papers across 4 main levels (basic-to-applied) of research. We discuss technical issues, limitations and caveats, comparisons against other lists of highly-cited researchers, and potential uses of this resource.

The online availability of scientific-literature databases and natural-language-processing (NLP) algorithms has enabled large-scale bibliometric studies within the field of scientometrics. Using NLP techniques and Thomson ISI reports, an initial analysis of the role of Alzheimer's disease (AD) within the neurosciences as well as a summary of the various research foci within the AD scientific community are presented. Citation analyses and productivity filters are applied to post-1984, AD-specific subsets of the PubMed and Thomson ISI Web-of-Science literature bases to algorithmically identify a pool of the top AD researchers. From the initial pool of AD investigators, top-100 rankings are compiled to assess productivity and impact. One of the impact and productivity metrics employed is an AD-specific H-index. Within the AD-specific H-index ranking, there are many cases of multiple AD investigators with similar or identical H-indices. In order to facilitate differentiation among investigators with equal or near-equal H indices, two derivatives of the H-index are proposed: the Second-Tier H-index and the Scientific Following H-index. Winners of two prestigious AD-research awards are highlighted, membership to the Institute of Medicine of the US National Academy of Sciences is acknowledged, and an analysis of highly-productive, high-impact, AD-research collaborations is presented.

The success of Alzheimer’s Disease: A Century of Scientific and Clinical Research, a retrospective of milestones in Alzheimer’s disease (AD) exploration coinciding with completion of the first century of AD research, led colleagues and our publisher to encourage us to examine the progress defining AD in the coming century. We employed a novel unbiased method to identify the most promising trends in the field of AD. Our method included scientometric indices and editorial guidance to assist us in developing a list of the 1,000 most cited articles in AD research in the five years since the centennial. From that list, we were able to identify the most active scientists during that time frame. Our goal was to cover the current interests in AD and reflect on the significance of these breakthroughs in the quest to understand, treat, and cure this devastating disease. While prior trends and advances in genetics, amyloid, tau, neuropathology, and oxidative stress continue as active areas, emergent areas of the transition from normal cognition to AD such as diagnostic imaging, biomarkers, metabolism, and lifestyle (areas conceived only a few years ago) now dominate the debate. Whether transformation from a field where basic molecular pathology was the sole avenue to one focused on clinical diagnosis and public health will result in therapeutic innovations remains to be seen.

Non-invasive stress testing is an important tool to diagnose patients suspected of having coronary disease. However, indications for these tests are broad. In this study, we examined appropriate use criteria and outcomes of stress testing to rule out coronary disease. 70 patients that were seen in