Nosso corpo nos pertence: a dialética do biológico e do social

Este artigo visa situar a discussão do biológico e do social no interior da análise da condição feminina. Com o surgimento das análises feministas, a hegemonia anterior das explicações biológicas é substituída por uma ênfase na construção social da identidade feminina. O determinismo biológico é repudiado, mesmo quando a identificação da mulher com o corpo e com a natureza e seu status secundário são considerados como universais. Neste processo de reelaboração do objeto, o papel ideológico da ciência é apontado, na medida em que a dominação masculina na ciência e na sociedade acompanhou a tendência histórica que relegou a questão da mulher à esfera natural. Embora uma crescente apreciação da construção social da atividade científica em si impulsione o abandono da dicotomia biológico/social ao nível conceitual, as diferenças entre homem e mulher na esfera da reprodução continuam a atuar. É argumentado que análise da reprodução requer caracterização dos sexos como entidades biossociais em relacionamento, situados em contextos históricos específicos, e que, na sociedade moderna, a mulher é sujeita a uma dupla contradição reprodutiva.<br>This article aims at reviewing the discussion of biological and social factors in the analysis of women's social condition. With the appearance of a feminist perspective, the dominance of earlier biologically-based explanations was substituted by an emphasis on the social construction of female identity. Even when women's identification with the body and with nature, and their secondary status, were considered universal, biological determinism was rejected. In this process of re-definition of the object of study, the ideological role of science was pointed out, since male dominance in science and society accompanied the historical tendency which relegated "the woman question" to the sphere of natural fact. Although growing awareness of the socially-constructed nature of scientific activity itself is producing a tendency to abandon the biological/social dichotomy at the conceptual level, differences between men and women in the reproductive sphere continue to exist. It is argued that analysis of reproduction requires characterization of the sexes as biosocial entities in relationship, situated in specific historical contexts, and that in modern society women are subject to a double reproductive contradiction

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc