Bibliographie

Cette bibliographie est proposée par les coordinateurs du dossier en complément de la liste des notices bibliographiques BGI. Adams W., Hutton J., 2007. People, Parks and Poverty: Political Ecology and Biodiversity Conservation. Conservation & Society, 5(2), p. 147-183 p. Adger W. N., Benjaminsen T. A., Brown K. et Svarstad H., 2001. Advancing a political ecology of global environmental discourses. Development and Change, 32 (4), p. 681-715. Agrawal A., 2005. Environmentality: Technologies of..

Engaging Students Engaging Industry Engaging Enterprise

A reflective piece on how a small team of students and academics gained more awareness of their own sense of enterprise and creativity. The case study examines the phases and crisis points of the whole event process and identifies some of the key learning outcomes for all involved

Territorialisation et pouvoir : la Political Ecology des territoires de conservation et de développement

Introduction Au nom de la conservation environnementale, de la sécurité alimentaire et du développement durable, un groupe diversifié d’acteurs public et privés s’affairent à diviser les Suds en territoires de conservation et de développement à différentes échelles. Plusieurs objectifs et processus conduisent ces stratégies spatiales à reconfigurer l’accès aux ressources, leur contrôle et leur gestion. La production de territoire pour contrôler les populations et les ressources est une pratiq..

The Grizzly, November 10, 1978

Physical Education Program To Change • Task Force Continues Recommendations • No Funds For Bomberger • Forum: High Strung • Hockey Not Safe • Staffer Clears Misinterpretation • Dining Service Transitions • Letters to the Editor • Portrait of the Professor: Gayle A. Byerly • For Whom The Walls Toil • Egdon Heath - A New Look For Monday Night • The Good Doctor Makes House Call To Protheatre • Eighteen Named to Who\u27s Who • Free V. D. Clinic • GM: Looking Good For \u2779 • Sports Profile: Keith Kemper • Thinclads Nab Third At MAC\u27s • Soccer Kicks Moravian • Bears Blast Dickinson • Gymnastics Get New Coach • Hockey Ends • Women\u27s B-Ball Preview • News in Brief: Senior Symposium Cancelled; Deans Attend State Conventionhttps://digitalcommons.ursinus.edu/grizzlynews/1006/thumbnail.jp

Predicting survival in malignant pleural mesothelioma using routine clinical and laboratory characteristics

Introduction The prognosis of malignant pleural mesothelioma (MPM) is poor, with a median survival of 8–12 months. The ability to predict prognosis in MPM would help clinicians to make informed decisions regarding treatment and identify appropriate research opportunities for patients. The aims of this study were to examine associations between clinical and pathological information gathered during routine care, and prognosis of patients with MPM, and to develop a 6-month mortality risk prediction model.Methods A retrospective cohort study of patients diagnosed with MPM at Queen Alexandra Hospital, Portsmouth, UK between December 2009 and September 2013. Multivariate analysis was performed on routinely available histological, clinical and laboratory data to assess the association between different factors and 6-month survival, with significant associations used to create a model to predict the risk of death within 6 months of diagnosis with MPM.Results 100 patients were included in the analysis. Variables significantly associated with patient survival in multivariate analysis were age (HR 1.31, 95% CI 1.09 to 1.56), smoking status (current smoker HR 3.42, 95% CI 1.11 to 4.20), chest pain (HR 2.14, 95% CI 1.23 to 3.72), weight loss (HR 2.13, 95% CI 1.18 to 3.72), platelet count (HR 1.05, 95% CI 1.00 to 1.10), urea (HR 2.73, 95% CI 1.31 to 5.69) and adjusted calcium (HR 1.47, 95% CI 1.10 to 1.94). The resulting risk model had a c-statistic value of 0.76. A Hosmer-Lemeshow test confirmed good calibration of the model against the original dataset.Conclusion Risk of death at 6 months in patients with a confirmed diagnosis of MPM can be predicted using variables readily available in clinical practice. The risk prediction model we have developed may be used to influence treatment decisions in patients with MPM. Further validation of the model requires evaluation of its performance on a separate dataset