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The need to align investments in health research and development (R&D) with public health demands is one of the most pressing global public health challenges. We aim to provide a comprehensive description of available data sources, propose a set of indicators for monitoring the global landscape of health R&D, and present a sample of country indicators on research inputs (investments), processes (clinical trials), and outputs (publications), based on data from international databases. Total global investments in health R&D (both public and private sector) in 2009 reached US$240 billion. Of the US$214 billion invested in high-income countries, 60% of health R&D investments came from the business sector, 30% from the public sector, and about 10% from other sources (including private non-profit organisations). Only about 1% of all health R&D investments were allocated to neglected diseases in 2010. Diseases of relevance to high-income countries were investigated in clinical trials seven-to-eight-times more often than were diseases whose burden lies mainly in low-income and middle-income countries. This report confirms that substantial gaps in the global landscape of health R&D remain, especially for and in low-income and middle-income countries. Too few investments are targeted towards the health needs of these countries. Better data are needed to improve priority setting and coordination for health R&D, ultimately to ensure that resources are allocated to diseases and regions where they are needed the most. The establishment of a global observatory on health R&D, which is being discussed at WHO, could address the absence of a comprehensive and sustainable mechanism for regular global monitoring of health R&D.

Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities. By paying attention to public perceptions of their publications, scientists can learn whether their research is stimulating positive scholarly and public thought. They can also become aware of potentially negative patterns of interest from groups that misinterpret their work in harmful ways, either willfully or unintentionally, and devise strategies for altering their messaging to mitigate these impacts. In this study, we collected 331,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communities engaging with each preprint on Twitter. We agnostically learned the characteristics of these audience sectors from keywords each user's followers provide in their Twitter biographies. We estimate that 96% of the preprints analyzed are dominated by academic audiences on Twitter, suggesting that social media attention does not always correspond to greater public exposure. We further demonstrate how our audience segmentation method can quantify the level of interest from nonspecialist audience sectors such as mental health advocates, dog lovers, video game developers, vegans, bitcoin investors, conspiracy theorists, journalists, religious groups, and political constituencies. Surprisingly, we also found that 10% of the preprints analyzed have sizable (>5%) audience sectors that are associated with right-wing white nationalist communities. Although none of these preprints appear to intentionally espouse any right-wing extremist messages, cases exist in which extremist appropriation comprises more than 50% of the tweets referencing a given preprint. These results present unique opportunities for improving and contextualizing the public discourse surrounding scientific research.

We have used 19.9 million papers over 5 decades and 2.1 million patents to demonstrate that teams increasingly dominate solo authors in the production of knowledge. Research is increasingly done in teams across nearly all fields. Teams typically produce more frequently cited research than individuals do, and this advantage has been increasing over time. Teams now also produce the exceptionally high-impact research, even where that distinction was once the domain of solo authors. These results are detailed for sciences and engineering, social sciences, arts and humanities, and patents, suggesting that the process of knowledge creation has fundamentally changed.

eHealth resources offer new opportunities for older adults to access health information online, connect with others with shared health interests, and manage their health. However, older adults often lack sufficient eHealth literacy to maximize their benefit from these resources. This review evaluates the research design, methods, and findings of eHealth literacy interventions for older adults. A systematic review of peer-reviewed research articles from 28 databases in 9 fields was carried out in January 2013. Four rounds of screening of articles in these databases resulted in a final sample of 23 articles. Findings indicated a significant gap in the literature for eHealth literacy interventions evaluating health outcomes as the outcome of interest, a lack of theory-based interventions, and few studies applied high-quality research design. Our findings emphasize the need for researchers to develop and assess theory-based interventions applying high-quality research design in eHealth literacy interventions targeting the older population.

Background To compare the efficacy and tolerability of paliperidone extended-release (ER) with risperidone immediate-release using propensity score methodology. Methods Six double-blind, randomized, placebo-controlled, short-term clinical trials for acute schizophrenia with availability of individual patient-level data were identified (3 per compound). Propensity score pairwise matching was used to balance observed covariates between the paliperidone ER and risperidone patient populations. Scores were generated using logistic regression models, with age, body mass index, race, sex, baseline Positive and Negative Syndrome Scale (PANSS) total score and baseline Clinical Global Impressions–Severity (CGI-S) score as factors. The dosage range of paliperidone ER (6-12 mg/day) was compared with 2 risperidone dosage ranges: 2-4 and 4-6 mg/day. The primary efficacy measure was change in PANSS total score at week 6 end point. Tolerability end points included adverse event (AE) reports and weight. AEs with rates ≥5% and with a ≥2% difference between paliperidone ER and risperidone were identified. Results Completion rates for placebo-treated subjects in paliperidone ER trials (n = 95) and risperidone trials (n = 122) groups were 36.8% and 51.6%, respectively; end point changes on PANSS total scores were similar (p = 0.768). Completion rates for subjects receiving paliperidone ER 6-12 mg/day (n = 179), risperidone 2-4 mg/day (n = 113) or risperidone 4-6 mg/day (n = 129) were 64.8%, 54.0% and 66.7%, respectively (placebo-adjusted rates: paliperidone ER vs risperidone 2-4 mg/day, p = 0.005; paliperidone ER vs risperidone 4-6 mg/day, p = 0.159). PANSS total score improvement with paliperidone ER was greater than with risperidone 2-4 mg/day (difference in mean change score, -6.7; p < 0.05) and similar to risperidone 4-6 mg/day (0.2; p = 0.927). Placebo-adjusted AEs more common with paliperidone ER were insomnia, sinus tachycardia and tachycardia; more common with risperidone were somnolence, restlessness, nausea, anxiety, salivary hypersecretion, akathisia, dizziness and nasal congestion. Weight changes with paliperidone ER and risperidone were similar (paliperidone ER vs risperidone 2-4 mg/day, p = 0.489; paliperidone ER vs risperidone 4-6 mg/day, p = 0.236). Conclusions This indirect database analysis suggested that paliperidone ER 6-12 mg/day may be more efficacious than risperidone 2-4 mg/day and as efficacious as risperidone 4-6 mg/day. The AE-adjusted incidence rates suggest differences between treatments that may be relevant for individual patients. Additional randomized, direct, head-to-head clinical trials are needed to confirm these findings.

Background Dropout (DO) is common in the treatment of eating disorders (EDs), but the reasons for this phenomenon remain unclear. This study is an extensive review of the literature regarding DO predictors in EDs. Methods All papers in PubMed, PsycINFO and Cochrane Library (1980-2009) were considered. Methodological issues and detailed results were analysed for each paper. After selection according to inclusion criteria, 26 studies were reviewed. Results The dropout rates ranged from 20.2% to 51% (inpatient) and from 29% to 73% (outpatient). Predictors of dropout were inconsistent due to methodological flaws and limited sample sizes. There is no evidence that baseline ED clinical severity, psychiatric comorbidity or treatment issues affect dropout. The most consistent predictor is the binge-purging subtype of anorexia nervosa. Good evidence exists that two psychological traits (high maturity fear and impulsivity) and two personality dimensions (low self-directedness, low cooperativeness) are related to dropout. Conclusion Implications for clinical practice and areas for further research are discussed. Particularly, these results highlight the need for a shared definition of dropout in the treatment of eating disorders for both inpatient and outpatient settings. Moreover, the assessment of personality dimensions (impulse control, self-efficacy, maturity fear and others) as liability factors for dropout seems an important issue for creating specific strategies to reduce the dropout phenomenon in eating disorders.

•According to the US National Readmissions Database:•Inpatient Laparoscopic Cholecystectomy accounted for 84.5% of cholecystectomy procedures with a 7% readmission rate, 0.0027% had an injury code, 0.46% required biliary to enteric anastomosis or biliary repair, and 2.98% required radiographic intervention (ERCP, PTC, IR Drain).•InpatientOpen Cholecystectomy accounted for 15.5% of all cholecystectomy procedures with a 14% readmission rate, 0.25% had an injury code, 6.27% required biliary to enteric anastomosis or biliary repair, and 5.8% required radiographic intervention (ERCP, PTC, IR Drain).

To understand what given scores of the Brief Psychiatric Rating Scale (BPRS) and the Positive and Negative Syndrome Scale (PANSS) mean from a clinical point of view is important for the translation of research results into practice. We therefore (a) compared the absolute change of the BPRS/PANSS with the Clinical Global Impressions Ratings (CGI) -improvement score and the change of the CGI severity score, (b) analyzed whether the severity of illness at baseline had an impact on the latter association, and (c) attempted to replicate previous BPRS findings using a completely different data set based upon the PANSS-derived BPRS. The method used was equipercentile linking of BPRS and CGI ratings from 14 drug trials in acutely ill patients with schizophrenia (n=5970). An absolute reduction of the BPRS/PANSS by approximately 10/15 points corresponded to a CGI change of 'minimally improved' and to a change of the CGI severity score by one severity step. However, the latter associations depended on the severity of symptoms at baseline. Less severely ill patients required less BPRS/PANSS total score reduction to achieve the same CGI-improvement score than more severely ill patients. This effect of initial severity was attenuated using percentage rather than absolute BPRS/PANSS reduction scores. The linking analysis between the absolute BPRS/PANSS reduction and the CGI may have an implication for the interpretation of efficacy differences found in clinical trials, and for sample size estimations. Clinicians seem to base CGI ratings on relative change rather than on absolute change of symptoms.

As of September 13, 2005, the International Committee of Medical Journal Editors has required that all trials submitted for publication be registered in a public database. This study examines the records registered in ClinicalTrials.gov from May 11 to October 11, 2005. The number of trials in the database approximately doubled, and the information about the trials became more specific. As of September 13, 2005, the International Committee of Medical Journal Editors required that all trials submitted for publication be registered in a public database. This study examines the records registered in ClinicalTrials.gov from May to October 2005. Concern about previously undisclosed safety problems with drugs such as paroxetine (Paxil, GlaxoSmithKline) and rofecoxib (Vioxx, Merck) has increased the public's desire for more complete information about clinical research studies. 1 , 2 The provision of basic information about clinical trial protocols in a publicly accessible registry and the public identification of all trials, whether or not their results are subsequently published, have been advocated as ways to address this issue. 3 – 6 Numerous groups have called for comprehensive registration by issuing statements or convening meetings to discuss policy and implementation details. 7 – 15 In the United States, the Food and Drug Administration (FDA) . . .

Numerous methods for causality assessment of adverse drug reactions (ADRs) have been published. The aim of this review is to provide an overview of these methods and discuss their strengths and weaknesses. We conducted electronic searches in MEDLINE (via PubMed), EMBASE and the Cochrane databases to find all assessment methods. Thirty-four different methods were found, falling into three broad categories: expert judgement/global introspection, algorithms and probabilistic methods (Bayesian approaches). Expert judgements are individual assessments based on previous knowledge and experience in the field using no standardized tool to arrive at conclusions regarding causality. Algorithms are sets of specific questions with associated scores for calculating the likelihood of a cause-effect relationship. Bayesian approaches use specific findings in a case to transform the prior estimate of probability into a posterior estimate of probability of drug causation. The prior probability is calculated from epidemiological information and the posterior probability combines this background information with the evidence in the individual case to come up with an estimate of causation. As a result of problems of reproducibility and validity, no single method is universally accepted. Different causality categories are adopted in each method, and the categories are assessed using different criteria. Because assessment methods are also not entirely devoid of individual judgements, inter-rater reliability can be low. In conclusion, there is still no method universally accepted for causality assessment of ADRs.