Morbidity and Mortality Analysis in the Treatment of Intertrochanteric Hip Fracture with Two Fixation Systems: Dynamic Hip Screw (DHS) or Trochanteric Fixation Nail Advance (TFNA)

Background: The aim of this study was to compare the clinical outcomes, complications, and mortality of patients with intertrochanteric hip fracture treated with dynamic hip screw (DHS) vs. trochanteric fixation nail advance (TFNA). Methods: We evaluated 152 patients with intertrochanteric fractures concerning age, sex, comorbidity, Charlson Index, preoperative gait, OTA/AO classification, time from fracture to surgery, blood loss, amount of blood replacement, changes in gait, full weight-bearing at hospital discharge, complications, and mortality. The final indicators encompassed the adverse effects linked to implants, postoperative complications, clinical healing or bone healing duration, and functional score. Results: The study included a total of 152 patients, out of which 78 (51%) received DHS treatment and 74 (49%) received TFNA treatment. The results of this study show that the TFNA group demonstrated superiority (p p p = 0.005) and severe dementia (p = 0.027). Mortality was higher in the DHS group; however, a longer time from diagnosis to surgery was also observed in this group (p < 0.005). Conclusions: The TFNA group has shown a higher success rate in achieving full weight-bearing at hospital discharge when treating trochanteric hip fractures. This makes it the preferred choice for treating unstable fractures in this region of the hip. Additionally, it is important to note that a longer time to surgery is associated with increased mortality in patients with hip fractures

Ethnic Identity and Power: Quilombos in Brazilian Public Policy

By specifically analyzing how residents of quilombos are affected by the new National Policy for Technical and Rural Extension Assistance (Pnater), this article examines the relation of power between public policies and ethnic identities. It discusses how the reformulated concept of development influences government activity in rural contexts and the adoption of compensatory actions for excluded portions of the population. It briefly presents the social, legal and conceptual trajectory of the quilombos, localizing the dynamics of power in the construction of quilombola identity, a project in constant re-elaboration by Brazilian society

Psicologia, política pública para a população quilombola e racismo

Resumo Este artigo sintetiza parte da pesquisa de doutorado realizada em uma das primeiras comunidades negras rurais do estado de São Paulo a conquistar título de terras quilombolas, o quilombo Maria Rosa. Objetiva-se compreender se, para aquela comunidade, a política pública de titulação de terras opera como dispositivo contra o racismo. Para atingir os objetivos propostos, foram realizadas observações e entrevistas fundamentadas pelas formulações de Enrique Pichon-Rivière e de outros autores da psicologia social e da psicologia de processos grupais, como René Kaës. Como resultado, constatou-se que a política convoca os moradores do quilombo em questão a entrarem em contato com os efeitos do escravismo e do racismo. Todavia, ainda falta uma política articulada, entre os diferentes níveis governamentais e voltada para a temática racial, que lhes dê o devido apoio

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

<div><p>Study of emotions in human–computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.</p></div