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Two experiments tested the idea that economic transactions among friends are influenced by a norm of generosity. Whereas a fairness norm ought to promote agreement in offers between buyers and sellers, a generosity norm should promote altruistic disagreements caused by sellers offering lower prices than buyers--a reversal of the well-known endowment effect. Supporting the present account, in both studies, offers among friends showed a reversal of the endowment effect. Moreover, disagreement in buying and selling offers was stronger among friends than among acquaintances, and (in study 2) the perceived generosity of offers, but not their perceived fairness, mediated this effect. Finally, both studies demonstrated that generosity is asymmetric: whereas selling offers among friends were generous by all comparative standards, buying offers were not. (Contains 3 figures, 4 tables and 2 footnotes.)

This paper presents a statistical approach to estimating the performance of a superscalar processor. Traditional trace-driven simulators can take a large amount time to conduct a performance evaluation of a machine, especially as the number of instructions increases. The result of this type of simulation is typically tied to the particular trace that was run. Elements such as dependencies, delays, and stalls are all a direct result of the particular trace being run, and can differ from trace to trace. This paper describes a model designed to separate simulation results from a specific trace. Rather than running a trace-driven simulation, a statistical model is employed, more specifically a Poisson distribution, to predict how these types of delay affects performance. Through the use of this statistical model, a performance evaluation can be conducted using a general code model, with specific stall rates, rather than a particular code trace. This model allows simulations to quickly run tens of millions of instructions and evaluate the performance of a particular micro-architecture while at the same time, allowing the flexibility to change the structure of the architecture.

Abstract Background Lactic acid-producing bacteria (LAB) probiotics demonstrate immunomodulating and anti-inflammatory effects and the ability to lessen the symptoms of arthritis in both animals and humans. This randomized, double-blind, placebo-controlled, parallel-design, clinical pilot trial was conducted to evaluate the effects of the LAB probiotic preparation, Bacillus coagulans GBI-30, 6086, on symptoms and measures of functional capacity in patients with rheumatoid arthritis (RA) in combination with pharmacological anti-arthritic medications. Methods Forty-five adult men and women with symptoms of RA were randomly assigned to receive Bacillus coagulans GBI-30, 6086 or placebo once a day in a double-blind fashion for 60 days in addition to their standard anti-arthritic medications. Arthritis activity was evaluated by clinical examination, the American College of Rheumatology (ACR) criteria, the Stanford Health Assessment Questionnaire Disability Index (HAQ-DI), and laboratory tests for erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP). Results Subjects who received Bacillus coagulans GBI-30, 6086 experienced borderline statistically significant improvement in the Patient Pain Assessment score (P = .052) and statistically significant improvement in Pain Scale (P = .046) vs placebo. Compared with placebo, Bacillus coagulans GBI-30, 6086 treatment resulted in greater improvement in patient global assessment and self-assessed disability; reduction in CRP; as well as the ability to walk 2 miles, reach, and participate in daily activities. There were no treatment-related adverse events reported throughout this study. Conclusions Results of this pilot study suggest that adjunctive treatment with Bacillus coagulans GBI-30, 6086 LAB probiotic appeared to be a safe and effective for patients suffering from RA. Because of the low study population size, larger trials are needed to verify these results. Trial registration ACTRN12609000435280

Background: Decisions about trial funding, ethical approval, or clinical practice guideline recommendations require expert judgments about the potential efficacy of new treatments. We tested whether individual and aggregated expert opinion of oncologists could predict reliably the efficacy of cancer treatments tested in randomized controlled trials. Materials and Methods: An international sample of 137 oncologists specializing in genitourinary, lung, and colorectal cancer provided forecasts on primary outcome attainment for five active randomized cancer trials within their subspecialty; skill was assessed using Brier scores (BS), which measure the average squared deviation between forecasts and outcomes. Results: A total of 40% of trials in our sample reported positive primary outcomes. Experts generally anticipated this overall frequency (mean forecast, 34%). Individual experts on average outperformed random predictions (mean BS = 0.29 [95% confidence interval (CI), 0.28–0.33] vs. 0.33) but underperformed prediction algorithms that always guessed 50% (BS = 0.25) or that were trained on base rates (BS = 0.19). Aggregating forecasts improved accuracy (BS = 0.25; 95% CI, 0.16–0.36]). Neither individual experts nor aggregated predictions showed appreciable discrimination between positive and nonpositive trials (area under the curve of a receiver operating characteristic curve, 0.52 and 0.43, respectively). Conclusion: These findings are based on a limited sample of trials. However, they reinforce the importance of basing research and policy decisions on the results of randomized trials rather than expert opinion or low‐level evidence. Implications for Practice: Predictions of oncologists, either individually or in the aggregate, did not anticipate reliably outcomes for randomized trials in cancer. These findings suggest that pooled expert opinion about treatment efficacy is no substitute for randomized trials. They also underscore the challenges of using expert opinion to prioritize interventions for clinical trials or to make recommendations in clinical practice guidelines. The effectiveness of decisions regarding approval of clinical trials relies partly on the ability of approvers to assess accurately the promise of treatments and prioritize those most promising. This study measured the ability of oncologist to predict whether cancer treatments would demonstrate efficacy in randomized trials. [ABSTRACT FROM AUTHOR]