Toddler-teacher interaction and teachers' sensitivity as predictors of toddler's development during COVID-19: Stability or change over time.

This study examined the stability and change patterns among toddlers' interactions with their teachers, teachers' sensitivity, and toddlers' development during the COVID-19 pandemic and the three plausible paths were tested to identify which of the study variables affected the development of toddlers in subsequent periods over time. The subjects of this study were 63 toddlers and 6 head teachers who attended a subsidized child care center, located in Kyunggi province, Korea. In order to carry out the research objectives, a non-experimental survey research design was undertaken, and the qualitative data was obtained via on-site observations by trained researchers. With regard to continuity and change patterns among the study variables toddlers who had been actively involved in initiating their verbal interactions with teachers showed more verbal interactions with their teachers even after 4 months passed. Also, it was found that the early (T1) social disposition of toddlers and the behavioral interaction that toddlers had initiated with teachers revealed a significant effect, supporting each of the three models, which are simultaneous, cumulative, and complex paths. The main results of this research support the contention that the interaction patterns vary by contexts of subject, time, and history, indicating that it would be useful to understand new competencies required for teachers within the context of the multi-faceted ramifications of the pandemic on toddler development.

Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.

As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of pixel distribution. Thus, widely available chest X-rays, in conjunction with clinical information, may be predictive of survival outcomes of patients with COVID-19, especially older, sicker patients, and can aid in disease management by providing additional information.