Children's Covid-19 writing and drawings and the existential imperative to educate for uncertainty.

The Covid-19 pandemic provokes a pedagogic crisis: education is ill-adapted to accommodate multiple uncertainties in students' lives. We examine how pandemic uncertainty is registered in a global collection of writing and drawing from 4 to 17-years-old, during the 2020 lockdowns. The study engages with Biesta's (2021) philosophical work on 'world-centred education', offering empirical examples from the collection that goes beyond the immediacy of everyday lives. We identify educational implications: acknowledging students' present experiences of the world; a slowing of pedagogical tempo; supporting students to navigate desires and fears; a language for expressing uncertainty; and engaging students in ethical and existential difficulty.

Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease.

Ground-glass opacities (GGOs) are a non-specific high-resolution computed tomography (HRCT) finding tipically observed in early Coronavirus disesase 19 (COVID-19) pneumonia. However, GGOs are also seen in other acute lung diseases, thus making challenging the differential diagnosis. To this aim, we investigated the performance of a radiomics-based machine learning method to discriminate GGOs due to COVID-19 from those due to other acute lung diseases. Two sets of patients were included: a first set of 28 patients (COVID) diagnosed with COVID-19 infection confirmed by real-time polymerase chain reaction (RT-PCR) between March and April 2020 having (a) baseline HRCT at hospital admission and (b) predominant GGOs pattern on HRCT; a second set of 30 patients (nCOVID) showing (a) predominant GGOs pattern on HRCT performed between August 2019 and April 2020 and (b) availability of final diagnosis. Two readers independently segmented GGOs on HRCTs using a semi-automated approach, and radiomics features were extracted using a standard open source software (PyRadiomics). Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented. PLS ß-weights of radiomics features, including the 5% features with the largest ß-weights in magnitude (top 5%), were obtained. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. The Youden's test assessed sensitivity and specificity of the classification. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The predictive model delivered an AUC of 0.868 (Youden's index = 0.68, sensitivity = 93%, specificity 75%, p = 4.2 × 10-7). Of the seven features included in the top 5% features, five were texture-related. A radiomics-based machine learning signature showed the potential to accurately differentiate GGOs due to COVID-19 pneumonia from those due to other acute lung diseases. Most of the discriminant radiomics features were texture-related. This approach may assist clinician to adopt the appropriate management early, while improving the triage of patients.