Acute pain in adults admitted to the emergency room: development and implementation of abbreviated guidelines.

AIM: Although acute pain is frequently reported by patients admitted to the emergency room, it is often insufficiently evaluated by physicians and is thus undertreated. With the aim of improving the care of adult patients with acute pain, we developed and implemented abbreviated clinical practice guidelines (CG) for the staff of nurses and physicians in our hospital's emergency room. METHODS: Our algorithm is based upon the practices described in the international literature and uses a simultaneous approach of treating acute pain in a rapid and efficacious manner along with diagnostic and therapeutic procedures. RESULTS: Pain was assessed using either a visual analogue scale (VAS) or a numerical rating scale (NRS) at ER admission and again during the hospital stay. Patients were treated with paracetamol and/or NSAID (VAS/NRS <4) or intravenous morphine (VAS/NRS > or =04). The algorithm also outlines a specific approach for patients with headaches to minimise the risks inherent to a non-specific treatment. In addition, our algorithm addresses the treatment of paroxysmal pain in patients with chronic pain as well as acute pain in drug addicts. It also outlines measures for pain prevention prior to minor diagnostic or therapeutic procedures. CONCLUSIONS: Based on published guidelines, an abbreviated clinical algorithm (AA) was developed and its simple format permitted a widespread implementation. In contrast to international guidelines, our algorithm favours giving nursing staff responsibility for decision making aspects of pain assessment and treatment in emergency room patients.

Patients' preferences on information and involvement in decision-making for gastrointestinal surgery

Objective: The relationship between physicians and patients has undergone¦important changes, and the current emancipation of patients has led to¦a real partnership in medical decision-making. This study aimed to assess¦patients' preferences on different aspects of decision-making during treatment¦and potential complications, as well as the amount and type of preoperative¦information before visceral surgery.¦Methods: Prospective non-randomized study based on a questionnaire given¦to 253 consecutive patients scheduled for elective GI surgery.¦Results: Concerning surgical complications or treatment in the intensive care¦unit, 64% of patients wished to take actively part in any medical decisions.¦The respective figures for cardiac resuscitation and treatment limitations were¦89% and 60%. About information, 73%, 77% and 47% of patients wish¦detailed information, information on a potential ICUhospitalization and cardiac¦resuscitation, respectively. Elderly and low-educated patients were significantly¦less interested in shared medical decision-making (p = 0·003 and 0·015) and in¦information receiving (p = 0·03 and 0·05). Similarly, involvement of the family¦in decision-making was significantly less important in elderly and male patients¦(p = 0·05 and 0·03 respectively). Neither the type of operation (minor or major)¦nor the severity of disease (malignancies vs. non-malignancies) was a significant¦factor for shared decision-making, information or family involvement.¦Conclusion: The vast majority of surgical patients clearly want to get adequate¦preoperative information about their disease and the planned treatment. They¦also consider it as crucial to be involved in any kind of decision-making for¦treatment and complications. The family's role is limited to support the treating¦physicians if the patient is unable to participate in taking decisions.

An algorithm for semi-infinite polynomial optimization

"Vegeu el resum a l'inici del document del fitxer adjunt."

An approximation algorithm for #k-SAT

"Vegeu el resum a l'inici del document del fitxer adjunt"

Evolution of decision-making and learning under fluctuating social environments

The capacity to learn to associate sensory perceptions with appropriate motor actions underlies the success of many animal species, from insects to humans. The evolutionary significance of learning has long been a subject of interest for evolutionary biologists who emphasize the bene¬fit yielded by learning under changing environmental conditions, where it is required to flexibly switch from one behavior to another. However, two unsolved questions are particularly impor¬tant for improving our knowledge of the evolutionary advantages provided by learning, and are addressed in the present work. First, because it is possible to learn the wrong behavior when a task is too complex, the learning rules and their underlying psychological characteristics that generate truly adaptive behavior must be identified with greater precision, and must be linked to the specific ecological problems faced by each species. A framework for predicting behavior from the definition of a learning rule is developed here. Learning rules capture cognitive features such as the tendency to explore, or the ability to infer rewards associated to unchosen actions. It is shown that these features interact in a non-intuitive way to generate adaptive behavior in social interactions where individuals affect each other's fitness. Such behavioral predictions are used in an evolutionary model to demonstrate that, surprisingly, simple trial-and-error learn¬ing is not always outcompeted by more computationally demanding inference-based learning, when population members interact in pairwise social interactions. A second question in the evolution of learning is its link with and relative advantage compared to other simpler forms of phenotypic plasticity. After providing a conceptual clarification on the distinction between genetically determined vs. learned responses to environmental stimuli, a new factor in the evo¬lution of learning is proposed: environmental complexity. A simple mathematical model shows that a measure of environmental complexity, the number of possible stimuli in one's environ¬ment, is critical for the evolution of learning. In conclusion, this work opens roads for modeling interactions between evolving species and their environment in order to predict how natural se¬lection shapes animals' cognitive abilities. - La capacité d'apprendre à associer des sensations perceptives à des actions motrices appropriées est sous-jacente au succès évolutif de nombreuses espèces, depuis les insectes jusqu'aux êtres hu¬mains. L'importance évolutive de l'apprentissage est depuis longtemps un sujet d'intérêt pour les biologistes de l'évolution, et ces derniers mettent l'accent sur le bénéfice de l'apprentissage lorsque les conditions environnementales sont changeantes, car dans ce cas il est nécessaire de passer de manière flexible d'un comportement à l'autre. Cependant, deux questions non résolues sont importantes afin d'améliorer notre savoir quant aux avantages évolutifs procurés par l'apprentissage. Premièrement, puisqu'il est possible d'apprendre un comportement incorrect quand une tâche est trop complexe, les règles d'apprentissage qui permettent d'atteindre un com¬portement réellement adaptatif doivent être identifiées avec une plus grande précision, et doivent être mises en relation avec les problèmes écologiques spécifiques rencontrés par chaque espèce. Un cadre théorique ayant pour but de prédire le comportement à partir de la définition d'une règle d'apprentissage est développé ici. Il est démontré que les caractéristiques cognitives, telles que la tendance à explorer ou la capacité d'inférer les récompenses liées à des actions non ex¬périmentées, interagissent de manière non-intuitive dans les interactions sociales pour produire des comportements adaptatifs. Ces prédictions comportementales sont utilisées dans un modèle évolutif afin de démontrer que, de manière surprenante, l'apprentissage simple par essai-et-erreur n'est pas toujours battu par l'apprentissage basé sur l'inférence qui est pourtant plus exigeant en puissance de calcul, lorsque les membres d'une population interagissent socialement par pair. Une deuxième question quant à l'évolution de l'apprentissage concerne son lien et son avantage relatif vis-à-vis d'autres formes plus simples de plasticité phénotypique. Après avoir clarifié la distinction entre réponses aux stimuli génétiquement déterminées ou apprises, un nouveau fac¬teur favorisant l'évolution de l'apprentissage est proposé : la complexité environnementale. Un modèle mathématique permet de montrer qu'une mesure de la complexité environnementale - le nombre de stimuli rencontrés dans l'environnement - a un rôle fondamental pour l'évolution de l'apprentissage. En conclusion, ce travail ouvre de nombreuses perspectives quant à la mo¬délisation des interactions entre les espèces en évolution et leur environnement, dans le but de comprendre comment la sélection naturelle façonne les capacités cognitives des animaux.

Role of disease activity for decision making ability in early multiple sclerosis

Background and purpose: Decision making (DM) has been defined as the process through which a person forms preferences, selects and executes actions, and evaluates the outcome related to a selected choice. This ability represents an important factor for adequate behaviour in everyday life. DM impairment in multiple sclerosis (MS) has been previously reported. The purpose of the present study was to assess DM in patients with MS at the earliest clinically detectable time point of the disease. Methods: Patients with definite (n=109) or possible (clinically isolated syndrome, CIS; n=56) MS, a short disease duration (mean 2.3 years) and a minor neurological disability (mean EDSS 1.8) were compared to 50 healthy controls aged 18 to 60 years (mean age 32.2) using the Iowa Gambling Task (IGT). Subjects had to select a card from any of 4 decks (A/B [disadvantageous]; C/D [advantageous]). The game consisted of 100 trials then grouped in blocks of 20 cards for data analysis. Skill in DM was assessed by means of a learning index (LI) defined as the difference between the averaged last three block indexes and first two block indexes (LI=[(BI-3+BI-4+BI-5)/3-(BI-1+B2)/2]). Non parametric tests were used for statistical analysis. Results: LI was higher in the control group (0.24, SD 0.44) than in the MS group (0.21, SD 0.38), however without reaching statistical significance (p=0.7). Interesting differences were detected when MS patients were grouped according to phenotype. A trend to a difference between MS subgroups and controls was observed for LI (p=0.06), which became significant between MS subgroups (p=0.03). CIS patients who confirmed MS diagnosis by presenting a second relapse after study entry showed a dysfunction in the IGT in comparison to the other CIS (p=0.01) and definite MS (p=0.04) patients. In the opposite, CIS patients characterised by not entirely fulfilled McDonald criteria at inclusion and absence of relapse during the study showed an normal learning pattern on the IGT. Finally, comparing MS patients who developed relapses after study entry, those who remained clinically stable and controls, we observed impaired performances only in relapsing patients in comparison to stable patients (p=0.008) and controls (p=0.03). Discussion: These results raise the assumption of a sustained role for both MS relapsing activity and disease heterogeneity (i.e. infra-clinical severity or activity of MS) in the impaired process of decision making.

Dynamic simulation of regulatory networks using SQUAD.

BACKGROUND: The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. RESULTS: We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. CONCLUSION: The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.

Prompt Payments by Government Departments pursuant to Government Decision No. S29296 of May 2009

Prompt Payments by Government Departments pursuant to Government Decision No. S29296 of May 2009 Click here to download PDF 22kb

Playing Second Fiddle: Expert Advice and Decision-Making in Switzerland

This thesis concerns the role of scientific expertise in the decision-making process at the Swiss federal level of government. It aims to understand how institutional and issue-specific factors influence three things: the distribution of access to scientific expertise, its valuation by participants in policy for- mulation, and the consequence(s) its mobilization has on policy politics and design. The theoretical framework developed builds on the assumption that scientific expertise is a strategic resource. In order to effectively mobilize this resource, actors require financial and organizational resources, as well as the conviction that it can advance their instrumental interests within a particular action situation. Institutions of the political system allocate these financial and organizational resources, influence the supply of scientific expertise, and help shape the venue of its deployment. Issue structures, in turn, condition both interaction configurations and the way in which these are anticipated by actors. This affects the perceived utility of expertise mobilization, mediating its consequences. The findings of this study show that the ability to access and control scientific expertise is strongly concentrated in the hands of the federal administration. Civil society actors have weak capacities to mobilize it, and the autonomy of institutionalized advisory bodies is limited. Moreover, the production of scientific expertise is undergoing a process of professionalization which strengthens the position of the federal administration as the (main) mandating agent. Despite increased political polarization and less inclu- sive decision-making, scientific expertise remains anchored in the policy subsystem, rather than being used to legitimate policy through appeals to the wider population. Finally, the structure of a policy problem matters both for expertise mobilization and for the latter's impact on the policy process, be- cause it conditions conflict structures and their anticipation. Structured problems result in a greater overlap between the principal of expertise mobilization and its intended audience, thereby increasing the chance that expertise shapes policy design. Conversely, less structured problems, especially those that involve conflicts about values and goals, reduce the impact of expertise.