Publications

<b><i>Introduction:</i></b> Three previous individual participant data meta-analyses (IPDMAs) reported that, compared to the Structured Clinical Interview for the DSM (SCID), alternative reference standards, primarily the Composite International Diagnostic Interview (CIDI) and the Mini International Neuropsychiatric Interview (MINI), tended to misclassify major depression status, when controlling for depression symptom severity. However, there was an important lack of precision in the results. <b><i>Objective:</i></b> To compare the odds of the major depression classification based on the SCID, CIDI, and MINI. <b><i>Methods:</i></b> We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis. <b><i>Results:</i></b> In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11–1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79–1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52–0.80). <b><i>Conclusions:</i></b> Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics.

Asymptotic waveform evaluation (AWE) is a waveform estimation technique which involves the computation of moments from a linear circuit followed by the generation of waveform estimates based on those moments. During moment computation a special case arises if there are capacitor cutsets or inductor loops. Methods proposed for handling these cases involve identifying the cutsets and loops and replacing one of the capacitors (inductors) with a dependent source. This note describes a different formulation of the problem. The elimination of redundant equations in order to compute moments can be viewed as the dual of the problem of reducing circuit equations to normal form. This formulation has a couple of interesting theoretical properties: (1) it formally handles all circuit topologies, including circuits with dependent sources for which the redundancies may be undetectable from an inspection of the circuit topology; and (2) it predicts that even networks without independent sources may have "hidden" particular solutions consisting of polynomials in t.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

We propose guidelines to evaluate the cumulative evidence of gene-environment (G × E) interactions in the causation of human cancer. Our approach has its roots in the HuGENet and IARC Monographs evaluation processes for genetic and environmental risk factors, respectively, and can be applied to common chronic diseases other than cancer. We first review issues of definitions of G × E interactions, discovery and modelling methods for G × E interactions, and issues in systematic reviews of evidence for G × E interactions, since these form the foundation for appraising the credibility of evidence in this contentious field. We then propose guidelines that include four steps: (i) score the strength of the evidence for main effects of the (a) environmental exposure and (b) genetic variant; (ii) establish a prior score category and decide on the pattern of interaction to be expected; (iii) score the strength of the evidence for interaction between the environmental exposure and the genetic variant; and (iv) examine the overall plausibility of interaction by combining the prior score and the strength of the evidence and interpret results. We finally apply the scheme to the interaction between NAT2 polymorphism and tobacco smoking in determining bladder cancer risk.

In this sample of highly prominent claims of sex-related differences in genetic associations, most claims were insufficiently documented or spurious, and claims with documented good internal and external validity were uncommon.

Student motivation is critical to learning and program retention in engineering, yet most introductory circuits classes present the material in an abstract manner that does little to inspire students. In Stanford's introductory circuits course, 58% of the students are not electrical engineering majors, and generally have little intrinsic motivation for learning circuits, since it is not their chosen field. Another 39% are undeclared, and using the course to get a feel for electrical engineering as a whole. Our traditional linear circuits class covers Kirchhoff’s laws, nodal analysis, Thévenin/Norton, first-order response in the time domain for RL and RC circuits, op-amps, and phasors. Although this provides an important foundation for electrical engineers, it is not neither helpful nor interesting to non-majors who will not take further circuits classes. Moreover, it conveys a very narrow view of EE for those scouting out the major. Four years ago, we completely redesigned our introductory circuits class to address these shortcomings. The new course is focused around a sequence of fun and practical lab projects, where students use what they are learning to build complete devices: a solar-powered cell phone charger, a trick box which turns itself off, an audio-controlled LED cube, and an electrocardiogram. The lectures explain the material just in time for each lab, beginning with linear circuits, but quickly detouring to explore diodes, solar cells, transistors, digital logic, and a bit of microcontroller programming. We return to linear circuits after introducing the frequency domain, and discuss filters and amplifiers. We explicitly teach students techniques for building physical circuits and devices, and labs are graded for quality of design and construction in addition to electrical functionality. This broader range of theoretical topics and practical skills provides students (especially non-majors) with a more powerful toolbox for building useful circuits. Realistic applications are further emphasized through the homework, exams, and a series of in-class "breaking breaks". There were several positive results after introducing the new course. We experienced a large demographic shift in the course, with students taking the class earlier in their career and before declaring a major. Student evaluations of the labs have been consistently positive, and a handful of students specifically cited the course as their reason for choosing to major in electrical engineering.

Abstract Meta‐research has become increasingly popular and has provided interesting insights on what can go well and what can go wrong with research practices and scientific studies. Many stakeholders are taking actions to try to solve problems and biases identified through meta‐research. However, very often there is little or no evidence that specific recommendations and actions may actually lead to improvements and a favorable benefit‐harm ratio. The current commentary offers an eclectic overview of what we have learned from meta‐research efforts (mostly observational, but also some quasi‐experimental and experimental work) and what the implications of this evidence may be for changing research practices. Areas discussed include the study (and differentiation) of genuine effects and biases, fraud (including the impact of new technologies), peer review, replication and reproducibility checks, transparency indicators, and the interface of research practices with reward systems. Meta‐research has offered on all of these fronts empirical evidence that sometimes pertains even to large effects of extreme biases. Continued surveys of research practices and results may offer timely updates of the status of research and its biases, as these may change markedly over time. Meta‐research should be seen as part of research, not separate from it, in their concurrent evolution.

Preventing psychosis in patients at clinical high risk may be a promising avenue for pre‐emptively ameliorating outcomes of the most severe psychiatric disorder. However, information on how each preventive intervention fares against other currently available treatment options remains unavailable. The aim of the current study was to quantify the consistency and magnitude of effects of specific preventive interventions for psychosis, comparing different treatments in a network meta‐analysis. PsycINFO, Web of Science, Cochrane Central Register of Controlled Trials, and unpublished/grey literature were searched up to July 18, 2017, to identify randomized controlled trials conducted in individuals at clinical high risk for psychosis, comparing different types of intervention and reporting transition to psychosis. Two reviewers independently extracted data. Data were synthesized using network meta‐analyses. The primary outcome was transition to psychosis at different time points and the secondary outcome was treatment acceptability (dropout due to any cause). Effect sizes were reported as odds ratios and 95% confidence intervals (CIs). Sixteen studies (2,035 patients, 57% male, mean age 20.1 years) reported on risk of transition. The treatments tested were needs‐based interventions (NBI); omega‐3 + NBI; ziprasidone + NBI; olanzapine + NBI; aripiprazole + NBI; integrated psychological interventions; family therapy + NBI; D‐serine + NBI; cognitive behavioural therapy, French &amp; Morrison protocol (CBT‐F) + NBI; CBT‐F + risperidone + NBI; and cognitive behavioural therapy, van der Gaag protocol (CBT‐V) + CBT‐F + NBI. The network meta‐analysis showed no evidence of significantly superior efficacy of any one intervention over the others at 6 and 12 months (insufficient data were available after 12 months). Similarly, there was no evidence for intervention differences in acceptability at either time point. Tests for inconsistency were non‐significant and sensitivity analyses controlling for different clustering of interventions and biases did not materially affect the interpretation of the results. In summary, this study indicates that, to date, there is no evidence that any specific intervention is particularly effective over the others in preventing transition to psychosis. Further experimental research is needed.

Amoxycillin and folate inhibitors are essentially as effective as more expensive antibiotics for the initial treatment of uncomplicated acute sinusitis. Small differences in efficacy may exist, but are unlikely to be clinically important.