<p>PDF - 45KB, Number of CEC papers in sub-areas of genomics by year.</p>
Evidence for benefit of antiretroviral resistance testing is sparse and limited to small short-term improvements of virologic response, mostly with GART and less with vPART. Current guidelines widely recommending the use of antiretroviral resistance testing in clinical practice are not commensurate with the available evidence.
There is an increasing trend to use commodity microprocessors as the compute engines in large-scale multiprocessors. However, given that the majority of the microprocessors are sold in the workstation market, not in the multiprocessor market, it is only natural that architectural features that benefit only multiprocessors are less likely to be adopted in commodity microprocessors. In this paper, we explore multiple-context processors, an architectural technique proposed to hide the large memory latency in multiprocessors. We show that while current multiple-context designs work reasonably well for multiprocessors, they are ineffective in hiding the much shorter uniprocessor latencies using the limited parallelism found in workstation environments. We propose an alternative design that combines the best features of two existing approaches, and present simulation results that show it yields better performance for both multiprogrammed workloads on a workstation and parallel applications on a multiprocessor. By addressing the needs of the workstation environment, our proposal makes multiple contexts more attractive for commodity microprocessors.
Abstract In 2012, the National Cancer Institute (NCI) engaged the scientific community to provide a vision for cancer epidemiology in the 21st century. Eight overarching thematic recommendations, with proposed corresponding actions for consideration by funding agencies, professional societies, and the research community emerged from the collective intellectual discourse. The themes are (i) extending the reach of epidemiology beyond discovery and etiologic research to include multilevel analysis, intervention evaluation, implementation, and outcomes research; (ii) transforming the practice of epidemiology by moving toward more access and sharing of protocols, data, metadata, and specimens to foster collaboration, to ensure reproducibility and replication, and accelerate translation; (iii) expanding cohort studies to collect exposure, clinical, and other information across the life course and examining multiple health-related endpoints; (iv) developing and validating reliable methods and technologies to quantify exposures and outcomes on a massive scale, and to assess concomitantly the role of multiple factors in complex diseases; (v) integrating “big data” science into the practice of epidemiology; (vi) expanding knowledge integration to drive research, policy, and practice; (vii) transforming training of 21st century epidemiologists to address interdisciplinary and translational research; and (viii) optimizing the use of resources and infrastructure for epidemiologic studies. These recommendations can transform cancer epidemiology and the field of epidemiology, in general, by enhancing transparency, interdisciplinary collaboration, and strategic applications of new technologies. They should lay a strong scientific foundation for accelerated translation of scientific discoveries into individual and population health benefits. Cancer Epidemiol Biomarkers Prev; 22(4); 508–16. ©2013 AACR.
They show how RISATs allow fairer access to participation, improve applicability and generalizability of results compared with traditional single-arm trials, and provide a form of randomized real-world evidence. At negligible added cost beyond already existing infrastructure, this approach may catalyze the early generation of evidence of higher value than that produced with traditional single-arm trials, increasing the credibility and validity of accelerated drug approval processes and enabling better health care decisions.
Explanation of how 92 studies provided data regarding 127 analyses. (DOC 36 kb)
We evaluated what guidance exists in the literature to improve the transparency of studies that make secondary use of health data. To find peer-reviewed papers, we searched PubMed and Google Scholar. To find institutional documents, we used our personal expertise to draft a list of health organizations and searched their websites. We quantitatively and qualitatively coded different types of research transparency: registration, methods reporting, results reporting, data sharing and code sharing. We found 56 documents that provide recommendations to improve the transparency of studies making secondary use of health data, mainly in relation to study registration ( n = 27) and/or methods reporting ( n = 39). Only three documents made recommendations on data sharing or code sharing. Recommendations for study registration and methods reporting mainly came in the form of structured documents like registration templates and reporting guidelines. Aside from the recommendations aimed directly at researchers, we also found recommendations aimed at the wider research community, typically on how to improve research infrastructure. Limitations or challenges of improving transparency were rarely mentioned, highlighting the need for more nuance in providing transparency guidance for studies that make secondary use of health data.
<title>Abstract</title> Background Scientific retractions remain rare but have become increasingly common. We have previously incorporated retraction data into Scopus-based databases of top-cited (top 2%) scientists to facilitate linkage of retractions with impact metrics at the individual scientist level. Here, we set out to explore whether gender disparities in the likelihood of having retractions exist, both among highly-cited authors and among all authors with ≥ 5 publications. Methods We conducted a descriptive cross-sectional bibliometric analysis of a Scopus-based authors database. We used NamSor to assign gender, retaining only results with a confidence > 85%. We examined the demographics of scientists with and without retractions among highly cited authors (career-long impact: n = 217,097) and among all other authors (n = 10,361,367). We stratified by publication age, field, country income level, and publication volume, and calculated gender-specific retraction rates and the relative propensity (R) of women versus men to have at least one retraction. Results Gender could be classified for 8,267,888 scientists. Among highly cited authors, 3.3% of men and 2.9% of women had at least one retraction; among all authors, the rate was 0.7% for both genders. Differences varied by field: women’s rates were at least one-third lower than men’s (R < 0.67) in Biology, Biomedical Research, and Psychology (R < 0.67), but higher (R > 1.33) in Economics, Engineering, and Information and Communication Technologies. Among highly cited authors, younger cohorts showed increasingly higher rates among men (4.2% men vs. 3.0% women in those starting to publish in 2002–2011; 8.7% men vs. 4.9% women in those starting post-2011). Country-level differences among highly cited authors were pronounced in some countries, as in Pakistan (28.7% men vs. 14.3% women). These differences were smaller among all authors. Conclusion Our analysis shows that gender differences in retraction rates exist but are modest. Field, country, and publication volume are stronger correlates. Structural and contextual factors likely drive retraction patterns and warrant further investigation.
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Misuse and misinterpretation of statistics result in statistical biases that affect the quality, clarity, relevance, and implications of communicated scientific information. Statistical tools are often suboptimally used in scientific papers, even in the best journals. The vast majority of published results are statistically significant, and even nonsignificant results are often spun as being important. Inferences based on P-values generate additional misconceptions. It is also common to focus on metrics that are more prone to exaggerated interpretation. Most of these problems are possible to solve or at least improve on. The prevalence of statistical biases has been used in attacks designed to discredit science’s validity. However, the use of rigorous statistical methods and their careful interpretation can be one of the strongest distinguishing features of good science and a powerful tool to sustain science’s integrity.
The issue of nonreplicable evidence has attracted considerable attention across biomedical and other sciences. This concern is accompanied by an increasing interest in reforming research incentives and practices. How to optimally perform these reforms is a scientific problem in itself, and economics has several scientific methods that can help evaluate research reforms. Here, we review these methods and show their potential. Prominent among them are mathematical modeling and laboratory experiments that constitute affordable ways to approximate the effects of policies with wide-ranging implications.
Among currently available screening tests for diseases where death is a common outcome, reductions in disease-specific mortality are uncommon and reductions in all-cause mortality are very rare or non-existent.
