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\"A surprising and fascinating look at how Black culture has been leveraged by corporate America, this book addresses some of today's most pressing public debates around allyship and diversity. Open the brochure for the Alvin Ailey American Dance Theater, and you'll see logos for corporations like American Express. Visit the website for the Apollo Theater and you'll notice acknowledgments to corporations like Coca Cola and Citibank. The Martin Luther King, Jr. Memorial and the National Museum of African American History and Culture, owe their very existence to large corporate donations from companies like General Motors. And while we can easily make sense of the need for such funding to keep cultural spaces afloat, less obvious are the reasons that corporations give to them. In Black Culture, Inc. Patricia A. Banks interrogates the notion that such giving is completely altruistic, and argues for a deeper understanding of the hidden trans

A decade after ClinicalTrials.gov launched a database for reporting results of clinical trials, the database includes results of approximately 36,000 trials. The authors discuss laws, regulations, and policies relevant to results reporting, trends over time in the frequency of reporting, and adherence to requirements for the completeness and quality of the results reported.

Failure to report the results of a clinical trial can distort the evidence base for clinical practice, breaches researchers' ethical obligations to participants, and represents an important source of research waste. The Food and Drug Administration Amendments Act (FDAAA) of 2007 now requires sponsors of applicable trials to report their results directly onto ClinicalTrials.gov within 1 year of completion. The first trials covered by the Final Rule of this act became due to report results in January, 2018. In this cohort study, we set out to assess compliance. We downloaded data for all registered trials on ClinicalTrials.gov each month from March, 2018, to September, 2019. All cross-sectional analyses in this manuscript were performed on data extracted from ClinicalTrials.gov on Sept 16, 2019; monthly trends analysis used archived data closest to the 15th day of each month from March, 2018, to September, 2019. Our study cohort included all applicable trials due to report results under FDAAA. We excluded all non-applicable trials, those not yet due to report, and those given a certificate allowing for delayed reporting. A trial was considered reported if results had been submitted and were either publicly available, or undergoing quality control review at ClinicalTrials.gov. A trial was considered compliant if these results were submitted within 1 year of the primary completion date, as required by the legislation. We described compliance with the FDAAA 2007 Final Rule, assessed trial characteristics associated with results reporting using logistic regression models, described sponsor-level reporting, examined trends in reporting, and described time-to-report using the Kaplan-Meier method. 4209 trials were due to report results; 1722 (40·9%; 95% CI 39·4–42·2) did so within the 1-year deadline. 2686 (63·8%; 62·4–65·3) trials had results submitted at any time. Compliance has not improved since July, 2018. Industry sponsors were significantly more likely to be compliant than non-industry, non-US Government sponsors (odds ratio [OR] 3·08 [95% CI 2·52–3·77]), and sponsors running large numbers of trials were significantly more likely to be compliant than smaller sponsors (OR 11·84 [9·36–14·99]). The median delay from primary completion date to submission date was 424 days (95% CI 412–435), 59 days higher than the legal reporting requirement of 1 year. Compliance with the FDAAA 2007 is poor, and not improving. To our knowledge, this is the first study to fully assess compliance with the Final Rule of the FDAAA 2007. Poor compliance is likely to reflect lack of enforcement by regulators. Effective enforcement and action from sponsors is needed; until then, open public audit of compliance for each individual sponsor may help. We will maintain updated compliance data for each individual sponsor and trial at fdaaa.trialstracker.net. Laura and John Arnold Foundation.

Mentorship, coaching, and sponsorship are critical for professional advancement in surgical careers. This study examines these different forms of support among surgeons and trainees. An electronic survey was developed and disseminated on social media and was hosted by the Association of Women Surgeons (AWS). Members and non-members of the AWS from diverse backgrounds, including trainees and students and those in academic and community practices were queried on various demographic and professional characteristics. The primary outcome of interest was access to mentorship, sponsorship and coaching. Chi-square, Fisher's exact tests, and logistic regression models were applied to analyze survey responses. Of 93 respondents, 48 ​% reported knowing the difference between mentors, coaches, and sponsors. 65 ​% of participants reported having a mentor, 28 ​% a sponsor, and 17 ​% a coach. Community-based surgeons were less likely to have mentors compared to academic surgeons (OR 0.09, 95 ​% CI 0.01–0.69, p ​= ​0.02). Administrative leaders were more likely to have sponsors (admin leaders 44.8 ​% vs. non admin 21.3 ​%, p ​= ​0.02) and coaches (admin leaders 31 ​% vs. non admin 11.5 ​%, p ​= ​0.02). Identifying people within one's organization and time constraints were the most common barriers to having that support. Efforts by institutions and surgical societies are needed to increase the availability of mentors, sponsors and coaches, particularly for community-based and early-career surgeons. •Mentorship was common (65 ​%), but coaching (17 ​%) and sponsorship (28 ​%) were infrequent among respondents.•Community-based physicians were significantly less likely to have access to mentorship compared to those in other settings, while those in academia were more likely to have sponsors and coaches.•The majority of professional support was obtained informally; only 27 ​% of mentorships arose through formal programs.•Surgeons with administrative roles were over twice as likely to have a coach or sponsor compared to peers without such roles.•Time constraints and institutional limitations were the most frequently reported barriers to accessing these kinds of support.

To map the current status of head-to-head comparative randomized evidence and to assess whether funding may impact on trial design and results. From a 50% random sample of the randomized controlled trials (RCTs) published in journals indexed in PubMed during 2011, we selected the trials with ≥100 participants, evaluating the efficacy and safety of drugs, biologics, and medical devices through a head-to-head comparison. We analyzed 319 trials. Overall, 238,386 of the 289,718 randomized subjects (82.3%) were included in the 182 trials funded by companies. Of the 182 industry-sponsored trials, only 23 had two industry sponsors and only three involved truly antagonistic comparisons. Industry-sponsored trials were larger, more commonly registered, used more frequently noninferiority/equivalence designs, had higher citation impact, and were more likely to have “favorable” results (superiority or noninferiority/equivalence for the experimental treatment) than nonindustry-sponsored trials. Industry funding [odds ratio (OR) 2.8; 95% confidence interval (CI): 1.6, 4.7] and noninferiority/equivalence designs (OR 3.2; 95% CI: 1.5, 6.6), but not sample size, were strongly associated with “favorable” findings. Fifty-five of the 57 (96.5%) industry-funded noninferiority/equivalence trials got desirable “favorable” results. The literature of head-to-head RCTs is dominated by the industry. Industry-sponsored comparative assessments systematically yield favorable results for the sponsors, even more so when noninferiority designs are involved.

Expedited market access for novel and efficacious drugs is warranted for patients. Since 2020, Swissmedic (The Swiss Agency for Therapeutic Products) has been participating in Project Orbis, a collaborative parallel-review programme launched by the US Food and Drug Administration (FDA) in 2019 to expedite patient access to cancer drugs. This programme allows regulatory agencies to remain independent in their decisions. We aimed to evaluate the effect of the first 2 years of Project Orbis from the Swissmedic perspective. In this comparative analysis, we compared submission gap (time between submission at the FDA and Swissmedic), review time, approval and consensus decision rate, and the approved indications between Swissmedic and the FDA for marketing authorisation applications (MAAs) in oncology submitted to Swissmedic through Project Orbis (Orbis MAAs) or outside of Project Orbis (non-Orbis MAAs) from Jan 1, 2020, to Dec 31, 2021. Swissmedic review time was evaluated with a decision until June 30, 2022. For the decision comparison analysis, non-Orbis oncology MAAs submitted and evaluated from Jan 1, 2009, to Dec 31, 2018 (referred to as the pre-Orbis era) were also considered. Inferential statistics were done using Wilcoxon rank-sum test and the 95% CI for the median was based on binomial distribution. For each hypothesis testing, the significance level was set to 5%. No correction for multiple testing was performed. We analysed the submission gap, review time, and regulatory decision for 31 Orbis MAAs and 41 non-Orbis MAAs during the Orbis era. The median submission gap was 33·0 days (95% CI 19·0–57·0) for Orbis MAAs versus 168·0 days (56·0–351·0) for non-Orbis MAAs (p<0·0001). The median review time at Swissmedic was 235·5 days (198·0–264·0) for Orbis MAAs versus 314·0 days (279·0–354·0) for non-Orbis MAAs (p=0·0002). Approval rates at Swissmedic were consistent between Orbis MAAs (20 [77%] of 26) and non-Orbis MAAs (31 [76%] of 41). The rate of consensus decisions between Swissmedic and the FDA was 21 (81%) of 26 for Orbis MAAs and 31 (76%) of 41 for non-Orbis MAAs. Swissmedic approval rates were lower for indication extensions than for new active substances for Orbis MAAs (13 [72%] of 18 vs seven [88%] of eight) and non-Orbis MAAs (17 [71%] of 24 vs 14 [82%] of 17). Divergent decisions between agencies were predominantly observed for indication extensions (11 [73%] of 15 divergent decisions). During the pre-Orbis era, Swissmedic approved 61 (88%) of 69 MAAs for new active substances. Submission gap and review time for oncology applications at Swissmedic were significantly reduced by participation in Project Orbis, and approval consensus decisions were increased between agencies. These findings suggests that participating in Project Orbis could lead to faster patient access to drugs. None.

Bipartite projections are used in a wide range of network contexts including politics (bill co-sponsorship), genetics (gene co-expression), economics (executive board co-membership), and innovation (patent co-authorship). However, because bipartite projections are always weighted graphs, which are inherently challenging to analyze and visualize, it is often useful to examine the ‘backbone,’ an unweighted subgraph containing only the most significant edges. In this paper, we introduce the R package backbone for extracting the backbone of weighted bipartite projections, and use bill sponsorship data from the 114 th session of the United States Senate to demonstrate its functionality.

Corporate sponsorship of events that support social values (e.g., human rights) help firms infuse their products with symbolic meaning, prolonging their life cycle. Yet, higher product prices might spark perceptions that the firm invests in social values for calculative or opportunistic motives, in which case event sponsorship is unlikely to deliver the expected benefits in the form of product longevity. This study explores this potential tension empirically, using data related to sponsored social events, entry prices, and product longevity for a U. S. cosmetics producer.

Exenatide once-weekly is an extended release formulation of exenatide, a glucagon-like peptide–1 receptor agonist, which can improve glycemic control, body weight, blood pressure, and lipid levels in patients with type 2 diabetes mellitus (T2DM). The EXenatide Study of Cardiovascular Event Lowering (EXSCEL) will compare the impact of adding exenatide once-weekly to usual care with usual care alone on major cardiovascular outcomes. EXSCEL is an academically led, phase III/IV, double-blind, pragmatic placebo-controlled, global trial conducted in 35 countries aiming to enrol 14,000 patients with T2DM and a broad range of cardiovascular risk over approximately 5 years. Participants will be randomized (1:1) to receive exenatide once-weekly 2 mg or matching placebo by subcutaneous injections. The trial will continue until 1,360 confirmed primary composite cardiovascular end points, defined as cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke, have occurred. The primary efficacy hypothesis is that exenatide once-weekly is superior to usual care with respect to the primary composite cardiovascular end point. EXSCEL is powered to detect a 15% relative risk reduction in the exenatide once-weekly group, with 85% power and a 2-sided 5% alpha. The primary safety hypothesis is that exenatide once-weekly is noninferior to usual care with respect to the primary cardiovascular composite end point. Noninferiority will be concluded if the upper limit of the CI is <1.30. EXSCEL will assess whether exenatide once-weekly can reduce cardiovascular events in patients with T2DM with a broad range of cardiovascular risk. It will also provide long-term safety information on exenatide once-weekly in people with T2DM. ClinicalTrials.gov Identifier: NCT01144338

The rapid growth of research in artificial intelligence (AI) and machine learning (ML) continues. However, it is unclear whether this growth reflects an increase in desirable study attributes or merely perpetuates the same issues previously raised in the literature. This study aims to evaluate temporal trends in AI/ML studies over time and identify variations that are not apparent from aggregated totals at a single point in time. We identified AI/ML studies registered on ClinicalTrials.gov with start dates between January 1, 2010, and December 31, 2023. Studies were included if AI/ML-specific terms appeared in the official title, detailed description, brief summary, intervention, primary outcome, or sponsors' keywords. Studies registered as systematic reviews and meta-analyses were excluded. We reported trends in AI/ML studies over time, along with study characteristics that were fast-growing and those that remained unchanged during 2010-2023. Of 3106 AI/ML studies, only 7.6% (n=235) were regulated by the US Food and Drug Administration. The most common study characteristics were randomized (56.2%; 670/1193; interventional) and prospective (58.9%; 1126/1913; observational) designs; a focus on diagnosis (28.2%; 335/1190) and treatment (24.4%; 290/1190); hospital/clinic (44.2%; 1373/3106) or academic (28%; 869/3106) sponsorship; and neoplasm (12.9%; 420/3245), nervous system (12.2%; 395/3245), cardiovascular (11.1%; 356/3245) or pathological conditions (10%; 325/3245; multiple counts per study possible). Enrollment data were skewed to the right: maximum 13,977,257; mean 16,962 (SD 288,155); median 255 (IQR 80-1000). The most common size category was 101-1000 (44.8%; 1372/3061; excluding withdrawn or missing), but large studies (n>1000) represented 24.1% (738/3061) of all studies: 29% (551/1898) of observational studies and 16.1% (187/1163) of trials. Study locations were predominantly in high-income countries (75.3%; 2340/3106), followed by upper-middle-income (21.7%; 675/3106), lower-middle-income (2.8%; 88/3106), and low-income countries (0.1%; 3/3106). The fastest-growing characteristics over time were high-income countries (location); Europe, Asia, and North America (location); diagnosis and treatment (primary purpose); hospital/clinic and academia (lead sponsor); randomized and prospective designs; and the 1-100 and 101-1000 size categories. Only 5.6% (47/842) of completed studies had results available on ClinicalTrials.gov, and this pattern persisted. Over time, there was an increase in not only the number of newly initiated studies, but also the number of completed studies without posted results. Much of the rapid growth in AI/ML studies comes from high-income countries in high-resource settings, albeit with a modest increase in upper-middle-income countries (mostly China). Lower-middle-income or low-income countries remain poorly represented. The increase in randomized or prospective designs, along with 738 large studies (n>1000), mostly ongoing, may indicate that enough studies are shifting from an in silico evaluation stage toward a prospective comparative evaluation stage. However, the ongoing limited availability of basic results on ClinicalTrials.gov contrasts with this field's rapid advancements and the public registry's role in reducing publication and outcome reporting biases.