64 research outputs found

    Integrating ecosystem services and resilience in sustainable forest management

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    In the Anthropocene era, humanity has substantially shaped the ecosystems to meet the growing demand for provisioning ecosystem services (ES) but at the same time, it has considerably altered the functioning of the Earth system leading to completely novel and unpredictable effects. Resilience, ES and sustainability have gained tremendous popularity, over the last decades, in the scientific, policy and management arenas to address these challenges. However, less attention has been paid to the relationships between ES and resilience and how these concepts interact with sustainability. We, therefore, analyzed the concepts of ES and resilience, their relationships, strengths and weaknesses to determine how resilience and ES could be together operationalized for sustainable forest management. This analysis is based on a literature review and on interviews with leading experts in the field of resilience and/or ES. These two sources of information are complementary as the scientific literature synthetizes a long thinking process while the interviews gather main thoughts and opinions. The analysis shows that resilience and ES are closely intertwined in several ways. On one hand, resilience determines the capacity of an ecosystem to provide ES in the face of disturbances and is influenced, in turn, by human actions taken to response to changes in ES. On the other hand, resilience is defined as the ability to maintain ES. Resilience is sometimes even included in some ES classifications. Finally, these two concepts are applied together in forest management, for example to maintain a desired set of ES in the face of disturbances. The resilience approach contributes to improve the ES approach and vice versa: the resilience approach introduces the temporal dimension in the ES approach while the ES approach helps integrating the multiple dimensions, scales, methods and points of views as well as their interactions in the resilience approach. Resilience may be mandatory to ES and vice versa as a loss of resilience/ES could jeopardize ES/resilience. In conclusion, pairing ES and resilience is essential to promote policies toward sustainable forest management. However, caution should be exercised to avoid traps of one concept overriding the other

    Integrating ecosystem services and resilience toward sustainability

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    In the Anthropocene era, humanity has considerably altered the functioning of Earth, resulting in global and inter-related social, economic and environmental crises. In response, resilience, ecosystem services (ES) and sustainability have gained tremendous popularity in the scientific, policy and management arenas. However, less attention has been paid to the relationships between ES and resilience and how these concepts interact with sustainability. We, therefore, analyzed the concepts of ES and resilience, their relationships, strengths and weaknesses to determine how resilience and ES could be together operationalized for sustainable forest management. This analysis, based on a literature review and on interviews with experts, shows that resilience and ES are closely intertwined. They meet in the social-ecological system perspective where resilience determines the capacity of the system to face disturbances and thus to provide ES and is influenced, in turn, by human actions taken to response to changes in ES. In a narrower sense, resilience is defined as the ability to maintain ES. Finally, in some ES classifications, resilience is treated as an ES among others. The resilience approach contributes to improve the ES approach and vice versa: resilience introduces the temporal dimension in ES while ES help integrating the multiple dimensions, scales, methods and points of views as well as their interactions in resilience. Resilience may be mandatory to ES and vice versa as a loss of resilience/ES could jeopardize ES/resilience. In conclusion, pairing ES and resilience is essential to promote policies toward sustainable forest management. However, caution should be exercised to avoid traps of one concept overriding the other

    Quality indicators for patients with traumatic brain injury in European intensive care units

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    Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measur

    Changing care pathways and between-center practice variations in intensive care for traumatic brain injury across Europe

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    Purpose: To describe ICU stay, selected management aspects, and outcome of Intensive Care Unit (ICU) patients with traumatic brain injury (TBI) in Europe, and to quantify variation across centers. Methods: This is a prospective observational multicenter study conducted across 18 countries in Europe and Israel. Admission characteristics, clinical data, and outcome were described at patient- and center levels. Between-center variation in the total ICU population was quantified with the median odds ratio (MOR), with correction for case-mix and random variation between centers. Results: A total of 2138 patients were admitted to the ICU, with median age of 49 years; 36% of which were mild TBI (Glasgow Coma Scale; GCS 13–15). Within, 72 h 636 (30%) were discharged and 128 (6%) died. Early deaths and long-stay patients (> 72 h) had more severe injuries based on the GCS and neuroimaging characteristics, compared with short-stay patients. Long-stay patients received more monitoring and were treated at higher intensity, and experienced worse 6-month outcome compared to short-stay patients. Between-center variations were prominent in the proportion of short-stay patients (MOR = 2.3, p < 0.001), use of intracranial pressure (ICP) monitoring (MOR = 2.5, p < 0.001) and aggressive treatme

    Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

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    Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

    Variation in Structure and Process of Care in Traumatic Brain Injury: Provider Profiles of European Neurotrauma Centers Participating in the CENTER-TBI Study.

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    INTRODUCTION: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. METHODS: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. RESULTS: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. CONCLUSION: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches
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