Ethnic Differences in Bladder Cancer Survival

ObjectiveTo examine trends in bladder cancer survival among whites, blacks, Hispanics, and Asian/Pacific Islanders in the United States over a 30-year period. Racial disparities in bladder cancer outcomes have been documented with poorer survival observed among blacks. Bladder cancer outcomes in other ethnic minority groups are less well described.MethodsFrom the Surveillance, Epidemiology and End Results cancer registry data, we identified patients diagnosed with transitional cell carcinoma of the bladder between 1975 and 2005. This cohort included 163,973 white, 7731 black, 7364 Hispanic, and 5934 Asian/Pacific Islander patients. We assessed the relationship between ethnicity and patient characteristics. Disease-specific 5-year survival was estimated for each ethnic group and for subgroups of stage and grade.ResultsBlacks presented with higher-stage disease than whites, Hispanics, and Asian/Pacific Islanders, although a trend toward earlier-stage presentation was observed in all groups over time. Five-year disease-specific survival was consistently worse for blacks than for other ethnic groups, even when stratified by stage and grade. Five-year disease-specific survival was 82.8% in whites compared with 70.2% in blacks, 80.7% in Hispanics, and 81.9% in Asian/Pacific Islanders. There was a persistent disease-specific survival disadvantage in black patients over time that was not seen in the other ethnic groups.ConclusionEthnic disparities in bladder cancer survival persist between whites and blacks, whereas survival in other ethnic minority groups appears similar to that of whites. Further study of access to care, quality of care, and treatment decision making among black patients is needed to better understand these disparities

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

<p>Abstract</p> <p>Background</p> <p>Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques.</p> <p>Methods</p> <p>In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques.</p> <p>Results</p> <p>Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve.</p> <p>Conclusion</p> <p>Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.</p

Bilateral Mastectomy versus Breast-Conserving Surgery for Early-Stage Breast Cancer: The Role of Breast Reconstruction

BACKGROUND: Although breast-conserving surgery is oncologically safe for women with early-stage breast cancer, mastectomy rates are increasing. The objective of this study was to examine the role of breast reconstruction in the surgical management of unilateral early-stage breast cancer. METHODS: A retrospective cohort study of women diagnosed with unilateral early-stage breast cancer (1998 to 2011) identified in the National Cancer Data Base was conducted. Rates of breast-conserving surgery, unilateral and bilateral mastectomy with contralateral prophylactic procedures (per 1000 early-stage breast cancer cases) were measured in relation to breast reconstruction. The association between breast reconstruction and surgical treatment was evaluated using a multinomial logistic regression, controlling for patient and disease characteristics. RESULTS: A total of 1,856,702 patients were included. Mastectomy rates decreased from 459 to 360 per 1000 from 1998 to 2005 (p < 0.01), increasing to 403 per 1000 in 2011 (p < 0.01). The mastectomy rates rise after 2005 reflects a 14 percent annual increase in contralateral prophylactic mastectomies (p < 0.01), as unilateral mastectomy rates did not change significantly. Each percentage point of increase in reconstruction rates was associated with a 7 percent increase in the probability of contralateral prophylactic mastectomies, with the greatest variation explained by young age(32 percent), breast reconstruction (29 percent), and stage 0 (5 percent). CONCLUSIONS: Since 2005, an increasing proportion of early-stage breast cancer patients have chosen mastectomy instead of breast-conserving surgery. This trend reflects a shift toward bilateral mastectomy with contralateral prophylactic procedures that may be facilitated by breast reconstruction availability

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

The psychological science accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data