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Introduction: Brain atrophy is a well-established MRI outcome for predicting clinical progression and monitoring treatment response in persons with multiple sclerosis (pwMS) at the group level. Despite the important progress made, the translation of brain atrophy assessment into clinical practice faces several challenges. Areas covered: In this review, the authors discuss technical- and subject-related barriers for implementing brain atrophy assessment as part of the clinical routine at the individual level. Substantial progress has been made to understand and mitigate technical barriers behind MRI acquisition. Numerous research and commercial segmentation techniques for volume estimation are available and technically validated, but their clinical value has not been fully established. A systematic assessment of subject-related barriers, which include genetic, environmental, biological, lifestyle, comorbidity, and aging confounders, is critical for the interpretation of brain atrophy measures at the individual subject level. Educating both medical providers and pwMS will help better clarify the benefits and limitations of assessing brain atrophy for disease monitoring and prognosis. Expert opinion: Integrating brain atrophy assessment into clinical practice for pwMS requires overcoming technical and subject-related challenges. Advances in MRI standardization, artificial intelligence, and clinician education will facilitate this process, improving disease management and potentially reducing long-term healthcare costs. [ABSTRACT FROM AUTHOR]

Abstract Background Due to clinical and methodological diversity, clinical studies included in meta-analyses often differ in ways that lead to differences in treatment effects across studies. Meta-regression analysis is generally recommended to explore associations between study-level characteristics and treatment effect, however, three key pitfalls of meta-regression may lead to invalid conclusions. Our aims were to determine the frequency of these three pitfalls of meta-regression analyses, examine characteristics associated with the occurrence of these pitfalls, and explore changes between 2002 and 2012. Methods A meta-epidemiological study of studies including aggregate data meta-regression analysis in the years 2002 and 2012. We assessed the prevalence of meta-regression analyses with at least 1 of 3 pitfalls: ecological fallacy, overfitting, and inappropriate methods to regress treatment effects against the risk of the analysed outcome. We used logistic regression to investigate study characteristics associated with pitfalls and examined differences between 2002 and 2012. Results Our search yielded 580 studies with meta-analyses, of which 81 included meta-regression analyses with aggregated data. 57 meta-regression analyses were found to contain at least one pitfall (70%): 53 were susceptible to ecological fallacy (65%), 14 had a risk of overfitting (17%), and 5 inappropriately regressed treatment effects against the risk of the analysed outcome (6%). We found no difference in the prevalence of meta-regression analyses with methodological pitfalls between 2002 and 2012, nor any study-level characteristic that was clearly associated with the occurrence of any of the pitfalls. Conclusion The majority of meta-regression analyses based on aggregate data contain methodological pitfalls that may result in misleading findings.

Background: Due to clinical and methodological diversity, clinical studies included in meta-analyses often differ in ways that lead to differences in treatment effects across studies. Meta-regression analysis is generally recommended to explore associations between study-level characteristics and treatment effect, however, three key pitfalls of meta-regression may lead to invalid conclusions. Our aims were to determine the frequency of these three pitfalls of meta-regression analyses, examine characteristics associated with the occurrence of these pitfalls, and explore changes between 2002 and 2012.Methods: A meta-epidemiological study of studies including aggregate data meta-regression analysis in the years 2002 and 2012. We assessed the prevalence of meta-regression analyses with at least 1 of 3 pitfalls: ecological fallacy, overfitting, and inappropriate methods to regress treatment effects against the risk of the analysed outcome. We used logistic regression to investigate study characteristics associated with pitfalls and examined differences between 2002 and 2012.Results: Our search yielded 580 studies with meta-analyses, of which 81 included meta-regression analyses with aggregated data. 57 meta-regression analyses were found to contain at least one pitfall (70%): 53 were susceptible to ecological fallacy (65%), 14 had a risk of overfitting (17%), and 5 inappropriately regressed treatment effects against the risk of the analysed outcome (6%). We found no difference in the prevalence of meta-regression analyses with methodological pitfalls between 2002 and 2012, nor any study-level characteristic that was clearly associated with the occurrence of any of the pitfalls.Conclusion: The majority of meta-regression analyses based on aggregate data contain methodological pitfalls that may result in misleading findings. [ABSTRACT FROM AUTHOR]

The article delineates the main methodological deficiencies and datasets available for comparing income inequality at the European and world level. The article is divided in two parts. In the first part of the article, I present the main datasets and methodological deficiencies when trying to compare income inequality in the world. Although efforts have been made in the last decades to generate comparative databases, the qualities of some indicators are rather doubtful and also there is no common ground on conceptualizing inequality indicators and measuring them, some of which are income-based, while others are household expenditure-based. Then, I present two classical studies on studying income inequality after the Washington consensus. In the second part of the article I sketch an overview of rising income inequality in Romania after 1989 by presenting some income inequality indicators and comparing them with other former CEE countries. However, I will pinpoint that Romania’s case is rather exceptional in the EU and, when comparing standard income inequality indicators, further explication needs to be delivered. I conclude that while income inequality is the result of macro-structural changes that occurred during the transition to market capitalism, political decisions are not to be played-down.

A fashionable concept, resilience is now a must in both academic research and management. However, its polysemy nourishes many debates on its uses, heuristics and operational relevance. The purpose of this article is not to bring these debates to a close. Starting from a cross-disciplinary state of the art, we point out the incompatibilities between certain meanings and uses of the term. These inconsistencies raise theoretical issues, leading some researchers to reject the term for that matter, especially those outside the cindynics field. The analysis of the concept also brings out some methodological pitfalls. These are evident when attempting to translate theory into operational terms. Resilience is indeed seen as a promising response to recurrent difficulties in risk management. Nevertheless, it solves them only partially and produces new ones. Lastly, its implementation involves ethical and political risks. The injunction to resilience that seems to prevail internationally is in fact implying a number of moral and ideological assumptions which are not always clearly stated and remain serious issues.