Sexual Size Dimorphism and Body Condition in the Australasian Gannet

Funding: The research was financially supported by the Holsworth Wildlife Research Endowment. Acknowledgments We thank the Victorian Marine Science Consortium, Sea All Dolphin Swim, Parks Victoria, and the Point Danger Management Committee for logistical support. We are grateful for the assistance of the many field volunteers involved in the study.Peer reviewedPublisher PD

Antidepressants and Breast and Ovarian Cancer Risk: A Review of the Literature and Researchers' Financial Associations with Industry

BACKGROUND: Antidepressant (AD) use has been purported to increase the risk of breast and ovarian cancer, although both epidemiological and pre-clinical studies have reported mixed results. Previous studies in a variety of biomedical fields have found that financial ties to drug companies are associated with favorable study conclusions. METHODS AND FINDINGS: We searched English-language articles in MEDLINE, PsychINFO, the Science Citations Index and the Cochrane Central Register of Controlled Clinical Trials (through November 2010). A total of 61 articles that assessed the relationship between breast and ovarian cancer and AD use and articles that examined the effect of ADs on cell growth were included. Multi-modal screening techniques were used to investigate researchers' financial ties with industry. A random effects meta-analysis was used to pool the findings from the epidemiological literature. Thirty-three percent (20/61) of the studies reported a positive association between ADs and cancer. Sixty-seven percent (41/61) of the studies reported no association or antiproliferative effect. The pooled odds ratio for the association between AD use and breast/ovarian cancer in the epidemiologic studies was 1.11 (95% CI, 1.03-1.20). Researchers with industry affiliations were significantly less likely than researchers without those ties to conclude that ADs increase the risk of breast or ovarian cancer. (0/15 [0%] vs 20/46 [43.5%] (Fisher's Exact test P = 0.0012). CONCLUSIONS: Both the pre-clinical and clinical data are mixed in terms of showing an association between AD use and breast and ovarian cancer. The possibility that ADs may exhibit a bi-phasic effect, whereby short-term use and/or low dose antidepressants may increase the risk of breast and ovarian cancer, warrants further investigation. Industry affiliations were significantly associated with negative conclusions regarding cancer risk. The findings have implications in light of the 2009 USPSTF guidelines for breast cancer screening and for the informed consent process

Why we need easy access to all data from all clinical trials and how to accomplish it

International calls for registering all trials involving humans and for sharing the results, and sometimes also the raw data and the trial protocols, have increased in recent years. Such calls have come, for example, from the Organization for Economic Cooperation and Development (OECD), the World Health Organization (WHO), the US National Institutes of Heath, the US Congress, the European Commission, the European ombudsman, journal editors, The Cochrane Collaboration, and several funders, for example the UK Medical Research Council, the Wellcome Trust, the Bill and Melinda Gates Foundation and the Hewlett Foundation

Reporting bias in medical research - a narrative review

Reporting bias represents a major problem in the assessment of health care interventions. Several prominent cases have been described in the literature, for example, in the reporting of trials of antidepressants, Class I anti-arrhythmic drugs, and selective COX-2 inhibitors. The aim of this narrative review is to gain an overview of reporting bias in the medical literature, focussing on publication bias and selective outcome reporting. We explore whether these types of bias have been shown in areas beyond the well-known cases noted above, in order to gain an impression of how widespread the problem is. For this purpose, we screened relevant articles on reporting bias that had previously been obtained by the German Institute for Quality and Efficiency in Health Care in the context of its health technology assessment reports and other research work, together with the reference lists of these articles

Evaluating multiple performance measures across several dimensions at a multi-facility outpatient center

Over the past several decades healthcare delivery systems have received increased pressure to become more efficient from both a managerial and patient perspective. Many researchers have turned to simulation to analyze the complex systems that exist within hospitals, but surprisingly few have published guidelines on how to analyze models with multiple performance measures. Moreover, the published literature has failed to address ways of analyzing performance along more than one dimension, such as performance by day of the week, patient type, facility, time period, or some combination of these attributes. Despite this void in the literature, understanding performance along these dimensions is critical to understanding the root of operational problems in almost any daily clinic operation. This paper addresses the problem of multiple responses in simulation experiments of outpatient clinics by developing a stratification framework and an evaluation construct by which managers can compare several operationally different outpatient systems across multiple performance measure dimensions. This approach is applied to a discrete-event simulation model of a real-life, large-scale oncology center to evaluate its operational performance as improvement initiatives affecting scheduling practices, process flow, and resource levels are changed. Our results show a reduction in patient wait time and resource overtime across multiple patient classes, facilities, and days of the week. This research has already proven to be successful as certain recommendations have been implemented and have improved the system-wide performance at the oncology center. Copyright Springer Science+Business Media, LLC 2007Multiple response, Terminating discrete-event simulation, Performance measures, Outpatient scheduling, Healthcare applications, Multi-facility service system, Oncology center system analysis,