2,497 publications from this institution
This paper describes Ariel, a system for analyzing voltage drops and current density in the power networks of CMOS VLSI circuits. Three main parts, a Magic-based resistance extractor, a Rsim-based current simulator, and a network analyzer, work in tandem to examine the current/voltage characteristics of the power networks with a minimum of manual effort from the designer. The performance of the system on several chips is given.
A phase-locked loop (PLL) and delay-locked loop (DLL) design with adaptively adjusting bandwidth enables optimal performance over a wide frequency range and across process, voltage, and temperature variations. A design methodology of such adaptive-bandwidth PLLs and DLLs is described. To assess the impact of each circuit parameter directly, we derive a discrete-time, open-loop dynamic model of the PLL/DLL that characterizes the change in output variables in response to the sampled error and we express the adaptive-bandwidth criteria in terms of the open-loop gains, instead of the traditional closed-loop parameters, /spl omega//sub n/ and /spl zeta/. Applying these criteria, we derive scaling equations for the charge-pump current and filter resistance that achieve adaptive bandwidth in charge-pump PLL/DLLs. We show that previously published adaptive-bandwidth PLL/DLLs, a self-biased PLL/DLL and a regulated-supply PLL/DLL, rely on the small-signal conductance tracking the large-signal conductance of the voltage-controlled oscillator/voltage-controlled delay-line and, thus, sustain constant /spl omega//sub n///spl omega//sub ref/ and /spl zeta/ only if the voltage swing is sufficiently higher than the device threshold voltage V/sub TH/. The paper also presents procedures to estimate the open-loop parameters from an open-loop impulse response of the PLL/DLL.
BACKGROUND Large, randomized controlled trials (RCTs) are essential in answering pivotal questions in child health. METHODS We created a bird’s eye view of all large, noncluster, nonvaccine pediatric RCTs with ≥1000 participants registered in ClinicalTrials.gov (last search January 9, 2020). We analyzed the funding sources, countries, outcomes, publication status, and correlation with the pediatric global burden of disease (GBD) for eligible trials. RESULTS We identified 247 large, nonvaccine, noncluster pediatric RCTs. Only 17 mega-trials with ≥5000 participants existed. Industry funding was involved in only 52 (21%) and exclusively funded 47 (19%) trials. Participants were from high-income countries (HICs) in 100 (40%) trials, from lower-middle-income countries (LMICs) in 122 (49%) trials, and from both HICs and LMICs in 19 (8%) trials; 6 trials did not report participants’ country location. Of trials conducted in LMIC, 43% of investigators were from HICs. Of non-LMIC participants trials (HIC or HIC and LMIC), 39% were multicountry trials versus 11% of exclusively LMIC participants trials. Few trials (18%; 44 of 247) targeted mortality as an outcome. 35% (58 of 164) of the trials completed ≥12 months were unpublished at the time of our assessment. The number of trials per disease category correlated well with pediatric GBD overall (ρ = 0.76) and in LMICs (ρ = 0.69), but not in HICs (ρ = 0.29). CONCLUSIONS Incentivization of investigator collaborations across diverse country settings, timely publication of results of large pediatric RCTs, and alignment with the pediatric GBD are of pivotal importance to ultimately improve child health globally.
John Ioannidis argues that problem base, context placement, information gain, pragmatism, patient centeredness, value for money, feasibility, and transparency define useful clinical research. He suggests most clinical research is not useful and reform is overdue.
Abstract In this paper, we compare the inference regarding the effectiveness of the various non-pharmaceutical interventions (NPIs) for COVID-19 obtained from three SIR models, all developed by the Imperial College COVID-19 Response Team. One model was applied to European countries and published in Nature 1 (model 1), concluding that complete lockdown was by far the most effective measure, responsible for 80% of the reduction in R t , and 3 million deaths were avoided in the examined countries. The Imperial College team applied a different model to the USA states 2 (model 2), and in response to our original submission, the Imperial team has proposed in a referee report a third model which is a hybrid of the first two models (model 3). We demonstrate that inference is highly nonrobust to model specification. In particular, inference regarding the relative effectiveness of NPIs changes substantially with the model and decision makers who are unaware of, or ignore, model uncertainty are underestimating the risk attached to any decisions based on that model. Our primary observation is that by applying to European countries the model that the Imperial College team used for the USA states (model 2), complete lockdown has no or little effect, since it was introduced typically at a point when R t was already very low. Moreover, using several state-of-the-art metrics for Bayesian model comparison, we demonstrate that model 2 (when applied to the European data) is better supported by the data than the model published in Nature 1 . In particular, serious doubt is cast on the conclusions in Flaxman et al. 1 , whether we examine the data up to May 5th (as in Flaxman et al. 1 ) or beyond the point when NPIs began to be lifted. Only by objectively considering a wide variety of models in a statistically principled manner, can one begin to address the effectiveness of NPIs such as lockdown. The approach outlined in this paper provides one such path.
Abstract Data continue to accumulate indicating that many systematic reviews are methodologically flawed, biased, redundant, or uninformative. Some improvements have occurred in recent years based on empirical methods research and standardization of appraisal tools; however, many authors do not routinely or consistently apply these updated methods. In addition, guideline developers, peer reviewers, and journal editors often disregard current methodological standards. Although extensively acknowledged and explored in the methodological literature, most clinicians seem unaware of these issues and may automatically accept evidence syntheses (and clinical practice guidelines based on their conclusions) as trustworthy. A plethora of methods and tools are recommended for the development and evaluation of evidence syntheses. It is important to understand what these are intended to do (and cannot do) and how they can be utilized. Our objective is to distill this sprawling information into a format that is understandable and readily accessible to authors, peer reviewers, and editors. In doing so, we aim to promote appreciation and understanding of the demanding science of evidence synthesis among stakeholders. We focus on well‐documented deficiencies in key components of evidence syntheses to elucidate the rationale for current standards. The constructs underlying the tools developed to assess reporting, risk of bias, and methodological quality of evidence syntheses are distinguished from those involved in determining overall certainty of a body of evidence. Another important distinction is made between those tools used by authors to develop their syntheses as opposed to those used to ultimately judge their work. Exemplar methods and research practices are described, complemented by novel pragmatic strategies to improve evidence syntheses. The latter include preferred terminology and a scheme to characterize types of research evidence. We organize best practice resources in a Concise Guide that can be widely adopted and adapted for routine implementation by authors and journals. Appropriate, informed use of these is encouraged, but we caution against their superficial application and emphasize their endorsement does not substitute for in‐depth methodological training. By highlighting best practices with their rationale, we hope this guidance will inspire further evolution of methods and tools that can advance the field.
While bias cannot be excluded, the meta-analysis suggests that the T allele may increase the risk of severe diastolic hypertension during pregnancy.
The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.
