Badri and colleagues claim that genotypic resistance testing has a greater benefit in patients with limited antiretroviral exposure, and that expert advice is likely to have a particularly beneficial effect in these patients. The evidence that they provide is derived from non-randomized data. We would caution that although these data may be useful, it is common for observational non-randomized studies to suggest greater treatment benefits than is shown by randomized trials [1]. Moreover, although their suggestions seem reasonable, the available randomized data do not really support them. First, contrary to what Badri and colleagues claim, a considerable proportion of patients in the five genotyping resistance testing trials had taken only one protease inhibitor before randomization. This percentage ranged from 33% in the NARVAL study [2] to 48% in the GART study [3] across the five trials. With the exception of the NARVAL trial [2], the mean or median duration of protease inhibitor regimens did not exceed 2 years. Therefore, even though the remaining treatment options were not as extensive in these trial populations as they are currently, considerable data are available on patients with limited previous exposure to highly active antiretroviral regimens. Data from the HAVANA [4], ARGENTA [5], and GART [3] trials show that the difference between the genotyping resistance testing and control arms is not significantly different in patients with limited previous antiretroviral experience versus those with more extensive previous antiretroviral experience. Only the NARVAL trial [3] discussed the presence of an interaction effect with the previous antiretroviral experience, but this applied only to the 12-week data (not the 24-week data), and even then it was not formally statistically significant (P = 0.17). Finally, the only long-term trial to date did not show any benefit from genotypic resistance testing despite the fact that it enrolled a considerable proportion of patients with limited previous exposure to protease inhibitors (68% had two or fewer protease inhibitors) [6]. Although one might anticipate that expert advice would make a difference, this is not shown by the randomized data. Table 4 of our meta-analysis [7] shows the difference in the proportion of patients with viral loads below detection with genotypic resistance testing versus controls. We apologize that because of an uncorrected typesetting error the title of the table refers to ‘virtual phenotypic resistance testing’ rather than the correct ‘genotypic resistance testing’. As shown, the effect size is very similar in trials that employed expert advice and in those that did not. Moreover, the only available head-to-head comparison of expert advice versus no expert advice [4] did not show any overall difference. Interaction terms in complex multivariate models should be viewed cautiously when they are based on only a small number of patients.
<div>Abstract<p><b>Background:</b> We report on the establishment of a web-based Cancer Epidemiology Descriptive Cohort Database (CEDCD). The CEDCD's goals are to enhance awareness of resources, facilitate interdisciplinary research collaborations, and support existing cohorts for the study of cancer-related outcomes.</p><p><b>Methods:</b> Comprehensive descriptive data were collected from large cohorts established to study cancer as primary outcome using a newly developed questionnaire. These included an inventory of baseline and follow-up data, biospecimens, genomics, policies, and protocols. Additional descriptive data extracted from publicly available sources were also collected. This information was entered in a searchable and publicly accessible database. We summarized the descriptive data across cohorts and reported the characteristics of this resource.</p><p><b>Results:</b> As of December 2015, the CEDCD includes data from 46 cohorts representing more than 6.5 million individuals (29% ethnic/racial minorities). Overall, 78% of the cohorts have collected blood at least once, 57% at multiple time points, and 46% collected tissue samples. Genotyping has been performed by 67% of the cohorts, while 46% have performed whole-genome or exome sequencing in subsets of enrolled individuals. Information on medical conditions other than cancer has been collected in more than 50% of the cohorts. More than 600,000 incident cancer cases and more than 40,000 prevalent cases are reported, with 24 cancer sites represented.</p><p><b>Conclusions:</b> The CEDCD assembles detailed descriptive information on a large number of cancer cohorts in a searchable database.</p><p><b>Impact:</b> Information from the CEDCD may assist the interdisciplinary research community by facilitating identification of well-established population resources and large-scale collaborative and integrative research. <i>Cancer Epidemiol Biomarkers Prev; 25(10); 1392–401. ©2016 AACR</i>.</p></div>
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.
Doctors' global assessments of the effects of treatments are on average similar to those of patients.
A fuller understanding of the social epidemiology of disease requires an extended description of the relationships between social factors and health indicators in a systematic manner. In the present study, we investigated the correlations between income and 330 indicators of physiological, biochemical, and environmental health in participants in the US National Health and Nutrition Examination Survey (NHANES) (1999-2006). We combined data from 3 survey waves (n = 249-23,649 for various indicators) to search for linear and nonlinear (quadratic) correlates of income, and we validated significant (P < 0.00015) correlations in an independent testing data set (n = 255-7,855). We validated 66 out of 330 factors, including infectious (e.g., hepatitis A), biochemical (e.g., carotenoids, high-density lipoprotein cholesterol), physiological (e.g., upper leg length), and environmental (e.g., lead, cotinine) measures. We found only a modest amount of association modification by age, race/ethnicity, and gender, and there was no association modification for blacks. The present study is descriptive, not causal. We have shown in our systematic investigation the crucial place income has in relation to health risk factors. Future research can use these correlations to better inform theory and studies of pathways to disease, as well as utilize these findings to understand when confounding by income is most likely to introduce bias.
Neoadjuvant therapy was apparently equivalent to adjuvant therapy in terms of survival and overall disease progression. Neoadjuvant therapy, compared with adjuvant therapy, was associated with a statistically significant increased risk of loco-regional recurrence when radiotherapy without surgery was adopted.
Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Internal Medicine HomeNew OnlineCurrent IssueFor Authors Podcast Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Internal Medicine journal
Abstract Aims We have previously described the European Medicines Agency’s (EMA) and the US Food and Drug Administration’s guidelines, each for a specific psychiatric indication, on how to design pivotal drug trials used in new drug applications. Here, we report on our efforts over 3 years to retrieve conflicts of interest declarations from EMA. We wanted to assess potential internal industry influence judged as the proportion of guideline committee members with industry conflicts of interest. Methods We submitted Freedom of Information requests in February 2020 to access EMA’s lists of committee members (and their declared conflicts of interest) involved in drafting the 13 ‘Clinical efficacy and safety’ guidelines available on EMA’s website pertaining to psychiatric indications. In our request, we did not specify the exact EMA committees. Here, we describe the received documents and report the proportion of members with industry interests (i.e. defined as any financial industry relationship). It is a follow-up paper to our first report ( http://doi.org/10.1017/S2045796021000147 ). Results After 2 years and 9 months (November 2022), the EMA sent us member lists and corresponding conflicts of interest declarations from the Committee for Medicinal Products for Human use (CHMP) from 2012, 2013 and 2017. These member lists pertained to 3 of the 13 requested guidelines (schizophrenia, depression and autism spectrum disorder). The 10 remaining guidelines were published before 2011 and EMA stated that they needed to require permission from their expert members (with unknown retrieval rate) and foresaw excessive workload and long wait. Therefore, we withdrew our request. The CHMPs from 2012, 2013 and 2017 had from 34 to 36 members; 39%–44% declared any interests and we judged 14%–18% as having industry interests. For the schizophrenia guideline, we identified two members with industry interests to companies who submitted feedback on the guideline. We did not receive declarations from the Central Nervous System (CNS) Working Party, the CHMP appointed expert group responsible for drafting and incorporating feedback into the guidelines. Conclusions After almost 3 years, we received information, which only partly addressed our request. We recommend EMA to improve transparency by publishing the author names and their corresponding conflicts of interest declarations directly in the ‘Clinical efficacy and safety’ guidelines and to not remove conflicts of interest declarations after 1 year from their website to reduce the risk of stealth corporate influence during the development of these influential guidelines.
Abstract Objective To determine the presence of a set of pre-specified traditional and non-traditional criteria used to assess scientists for promotion and tenure in faculties of biomedical sciences among universities worldwide. Design Cross sectional study. Setting International sample of universities. Participants 170 randomly selected universities from the Leiden ranking of world universities list. Main outcome measure Presence of five traditional (for example, number of publications) and seven non-traditional (for example, data sharing) criteria in guidelines for assessing assistant professors, associate professors, and professors and the granting of tenure in institutions with biomedical faculties. Results A total of 146 institutions had faculties of biomedical sciences, and 92 had eligible guidelines available for review. Traditional criteria of peer reviewed publications, authorship order, journal impact factor, grant funding, and national or international reputation were mentioned in 95% (n=87), 37% (34), 28% (26), 67% (62), and 48% (44) of the guidelines, respectively. Conversely, among non-traditional criteria, only citations (any mention in 26%; n=24) and accommodations for employment leave (37%; 34) were relatively commonly mentioned. Mention of alternative metrics for sharing research (3%; n=3) and data sharing (1%; 1) was rare, and three criteria (publishing in open access mediums, registering research, and adhering to reporting guidelines) were not found in any guidelines reviewed. Among guidelines for assessing promotion to full professor, traditional criteria were more commonly reported than non-traditional criteria (traditional criteria 54.2%, non-traditional items 9.5%; mean difference 44.8%, 95% confidence interval 39.6% to 50.0%; P=0.001). Notable differences were observed across continents in whether guidelines were accessible (Australia 100% (6/6), North America 97% (28/29), Europe 50% (27/54), Asia 58% (29/50), South America 17% (1/6)), with more subtle differences in the use of specific criteria. Conclusions This study shows that the evaluation of scientists emphasises traditional criteria as opposed to non-traditional criteria. This may reinforce research practices that are known to be problematic while insufficiently supporting the conduct of better quality research and open science. Institutions should consider incentivising non-traditional criteria. Study registration Open Science Framework ( https://osf.io/26ucp/?view_only=b80d2bc7416543639f577c1b8f756e44 ).
