The – switch: an empirical account of the anglicisation of a Scots variant in Scotland during the sixteenth and seventeenth centuries

2019 ◽  
Vol 24 (1) ◽  
pp. 211-236
Author(s):  
SARAH VAN EYNDHOVEN ◽  
LYNN CLARK

This article explores the anglicisation of the Scots language between the sixteenth and eighteenth centuries, focusing on the variation between the orthographic clusters <quh-> and <wh-> found in relative and interrogative clause markers. Using modern statistical techniques, we provide the most comprehensive empirical analysis of this variation so far in the Helsinki Corpus of Older Scots (Meurman-Solin 1995). By combining the techniques of Variability-Based Neighbour Clustering (Gries & Hilpert 2008, 2010, 2012) with mixed-effects logistic regression modelling (Baayen et al.2008), we uncover a different trajectory of change than that which has previously been reported for this feature (Meurman-Solin 1993, 1997). We argue that by using modern methods of data reduction and statistical modelling, we can present a picture of language change in Scots that is more fine-grained than previous studies which use only descriptive statistics.

QJM ◽  
2009 ◽  
Vol 103 (1) ◽  
pp. 23-32 ◽  
Author(s):  
B. Silke ◽  
J. Kellett ◽  
T. Rooney ◽  
K. Bennett ◽  
D. O’Riordan

2021 ◽  
Vol 8 ◽  
Author(s):  
Robert A. Reed ◽  
Andrei S. Morgan ◽  
Jennifer Zeitlin ◽  
Pierre-Henri Jarreau ◽  
Héloïse Torchin ◽  
...  

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.


2019 ◽  
Vol 146 ◽  
pp. 962-976 ◽  
Author(s):  
Helios Chiri ◽  
Ana Julia Abascal ◽  
Sonia Castanedo ◽  
Raul Medina

2018 ◽  
Vol 52 ◽  
pp. 29-30
Author(s):  
Christian Rønn Hansen ◽  
Anders Bertelsen ◽  
Ruta Zukauskaite ◽  
Lars Johnsen ◽  
Uffe Bernchou ◽  
...  

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