Bewertung des Risikos für Bias in kontrollierten Studien [Assessment of risk of bias in controlled studies].

BACKGROUND: Practicing physicians are faced with many medical decisions daily. These are mainly influenced by personal experience but should also consider patient preferences and the scientific evidence reflected by a constantly increasing number of medical publications and guidelines. With the objective of optimal medical treatment, the concept of evidence-based medicine is founded on these three aspects. It should be considered that there is a high risk of misinterpreting evidence, leading to medical errors and adverse effects without knowledge of the methodological background. OBJECTIVES: This article explains the concept of systematic error (bias) and its importance. Causes and effects as well as methods to minimize bias are discussed. This information should impart a deeper understanding, leading to a better assessment of studies and implementation of its recommendations in daily medical practice. CONCLUSION: Developed by the Cochrane Collaboration, the risk of bias (RoB) tool is an assessment instrument for the potential of bias in controlled trials. Good handling, short processing time, high transparency of judgements and a graphical presentation of findings that is easily comprehensible are among its strengths. Attached to this article the German translation of the RoB tool is published. This should facilitate the applicability for non-experts and moreover, support evidence-based medical decision-making.

Limitations and information needs for engineered nanomaterial-specific exposure estimation and scenarios: recommendations for improved reporting practices

The aim of this paper is to describe the process and challenges in building exposure scenarios for engineered nanomaterials (ENM), using an exposure scenario format similar to that used for the European Chemicals regulation (REACH). Over 60 exposure scenarios were developed based on information from publicly available sources (literature, books, and reports), publicly available exposure estimation models, occupational sampling campaign data from partnering institutions, and industrial partners regarding their own facilities. The primary focus was on carbon-based nanomaterials, nano-silver (nano-Ag) and nano-titanium dioxide (nano-TiO2), and included occupational and consumer uses of these materials with consideration of the associated environmental release. The process of building exposure scenarios illustrated the availability and limitations of existing information and exposure assessment tools for characterizing exposure to ENM, particularly as it relates to risk assessment. This article describes the gaps in the information reviewed, recommends future areas of ENM exposure research, and proposes types of information that should, at a minimum, be included when reporting the results of such research, so that the information is useful in a wider context.

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

Robotic-Assisted Surgery Improves the Quality of Total Mesorectal Excision for Rectal Cancer Compared to Laparoscopy: Results of a Case-Controlled Analysis.

BACKGROUND: The use of a robotic surgical system is claimed to allow precise traction and counter-traction, especially in a narrow pelvis. Whether this translates to improvement of the quality of the resected specimen is not yet clear. The aim of the study was to compare the quality of the TME and the short-term oncological outcome between robotic and laparoscopic rectal cancer resections. METHODS: 20 consecutive robotic TME performed in a single institution for rectal cancer (Rob group) were matched 1:2 to 40 laparoscopic resections (Lap group) for gender, body mass index (BMI), and distance from anal verge on rigid proctoscopy. The quality of TME was assessed by 2 blinded and independent pathologists and reported according to international standardized guidelines. RESULTS: Both samples were well matched for gender, BMI (median 25.9 vs. 24.2 kg/m(2), p = 0.24), and level of the tumor (4.1 vs. 4.8 cm, p = 0.20). The quality of the TME was better in the Robotic group (complete TME: 95 vs. 55 %; p = 0.0003, nearly complete TME 5 vs. 37 %; p = 0.04, incomplete TME 0 vs. 8 %, p = 0.09). A trend for lower positive circumferential margin was observed in the Robotic group (10 vs. 25 %, p = 0.1). CONCLUSIONS: These results suggest that robotic-assisted surgery improves the quality of TME for rectal cancer. Whether this translates to better oncological outcome needs to be further investigated.