Home literacy environment and early literacy development across languages varying in orthographic consistency

We examined the relation between home literacy environment (HLE) and early literacy development in a sample of children learning four alphabetic orthographies varying in orthographic consistency (English, Dutch, German, and Greek). Seven hundred and fourteen children were followed from Grade 1 to Grade 2 and tested on emergent literacy skills (vocabulary, letter knowledge, and phonological awareness) at the beginning of Grade 1 and on word reading fluency and spelling at the end of Grade 1, the beginning of Grade 2, and the end of Grade 2. Their parents responded to a questionnaire assessing HLE [parent teaching (PT), shared book reading (SBR), access to literacy resources (ALR)] at the beginning of Grade 1. Results showed first that PT was associated with letter knowledge or phonological awareness in Dutch and Greek, while ALR was associated with emergent literacy skills in all languages. SBR did not predict any cognitive or early literacy skills in any language. Second, PT and ALR had indirect effects on literacy outcomes via different emergent literacy skills in all languages. These findings suggest that not all HLE components are equally important for emergent literacy skills, reading fluency, and spelling. No specific trend in the role of orthographic consistency in the aforementioned relations emerged, which suggests that other factors may account for the observed differences across languages when children start receiving formal reading instruction in Grade 1

The use and applicability of machine learning algorithms in predicting the surgical outcome for patients with benign prostatic enlargement. Which model to use?

Objectives: Artificial intelligence (AI) is increasingly used in medicine, but data on benign prostatic enlargement (BPE) management are lacking. This study aims to test the performance of several machine learning algorithms, in predicting clinical outcomes during BPE surgical management. Methods: Clinical data were extracted from a prospectively collected database for 153 men with BPE, treated with transurethral resection (monopolar or bipolar) or vaporization of the prostate. Due to small sample size, we applied a method for increasing our dataset, Synthetic Minority Oversampling Technique (SMOTE). The new dataset created with SMOTE has been expanded by 453 synthetic instances, in addition to the original 153. The WEKA Data Mining Software was used for constructing predictive models, while several appropriate statistical measures, like Correlation coefficient (R), Mean Absolute Error (MAE), Root Mean-Squared Error (RMSE), were calculated with several supervised regression algorithms - techniques (Linear Regression, Multilayer Perceptron, SMOreg, k-Nearest Neighbors, Bagging, M5Rules, M5P - Pruned Model Tree, and Random forest). Results: The baseline characteristics of patients were extracted, with age, prostate volume, method of operation, baseline Qmax and baseline IPSS being used as independent variables. Using the Random Forest algorithm resulted in values of R, MAE, RMSE that indicate the ability of these models to better predict % Qmax increase. The Random Forest model also demonstrated the best results in R, MAE, RMSE for predicting % IPSS reduction. Conclusions: Machine Learning techniques can be used for making predictions regarding clinical outcomes of surgical BPRE management. Wider-scale validation studies are necessary to strengthen our results in choosing the best model. © 2021 Edizioni Scripta Manent s.n.c.. All rights reserved

Phonological awareness and rapid automatized naming as longitudinal predictors of reading in five alphabetic orthographies with varying degrees of consistency

Although phonological awareness (PA) and rapid automatized naming (RAN) are confirmed as early predictors of reading in a large number of orthographies, it is as yet unclear whether the predictive patterns are universal or language specific. This was examined in a longitudinal study across Grades 1 and 2 with 1,120 children acquiring one of five alphabetic orthographies with different degrees of orthographic complexity (English, French, German, Dutch, and Greek). Path analyses revealed that a universal model could not be confirmed. When we specified the best-fitting model separately for each language, RAN was a consistent predictor of reading fluency in all orthographies, whereas the association between PA and reading was complex and mostly interactive. We conclude that RAN taps into a language-universal cognitive mechanism that is involved in reading alphabetic orthographies (independent of complexity), whereas the PA–reading relationship depends on many factors like task characteristics, developmental status, and orthographic complexity

Using machine learning techniques to predict antimicrobial resistance in stone disease patients

Purpose: Artificial intelligence is part of our daily life and machine learning techniques offer possibilities unknown until now in medicine. This study aims to offer an evaluation of the performance of machine learning (ML) techniques, for predicting bacterial resistance in a urology department. Methods: Data were retrieved from laboratory information system (LIS) concerning 239 patients with urolithiasis hospitalized in the urology department of a tertiary hospital over a 1-year period (2019): age, gender, Gram stain (positive, negative), bacterial species, sample type, antibiotics and antimicrobial susceptibility. In our experiments, we compared several classifiers following a tenfold cross-validation approach on 2 different versions of our dataset; the first contained only information of Gram stain, while the second had knowledge of bacterial species. Results: The best results in the balanced dataset containing Gram stain, achieve a weighted average receiver operator curve (ROC) area of 0.768 and F-measure of 0.708, using a multinomial logistic regression model with a ridge estimator. The corresponding results of the balanced dataset, that contained bacterial species, achieve a weighted average ROC area of 0.874 and F-measure of 0.783, with a bagging classifier. Conclusions: Artificial intelligence technology can be used for making predictions on antibiotic resistance patterns when knowing Gram staining with an accuracy of 77% and nearly 87% when identifying specific microorganisms. This knowledge can aid urologists prescribing the appropriate antibiotic 24–48 h before test results are known. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature