Multiple oppositions intervention: effective phonological treatment of two children with cleft lip and palate and severe speech sound disorder.

The purpose of this study was to investigate if multiple oppositions intervention (MOI) generated widespread change in the phonological systems of two children with cleft lip and palate (CLP) and severe speech sound disorders (SSD). We treated two children (ages 5;4 and 5;6) with CLP and severe SSD using MOI for 24 and 29 sessions. We measured the percentage consonants correct (PCC) for target consonants and untreated consonants in non-treatment single words, as well as PCC for connected speech. Data points were collected in the baseline, intervention, and maintenance phase with post-tests conducted immediately after intervention and at 1, 3, 6 and 12 months. Two speech and language therapists (SLTs) unfamiliar with the children performed phonetic transcriptions, and we calculated intra- and inter-rater agreement. We graphed the data, and used permutation tests to analyse the probability that the observed increases in PCC were due to random chance. Both children experienced considerable improvements in PCC across all measures at the first post-test, supporting the impact of MOI on their entire phonological system. The PCC continued to increase during the maintenance phase. By the final post-test, the PCC in connected speech exceeded 90% for both children, reducing their SSD classification to mild. Our findings support that a phonological, contrastive intervention approach targeting multiple consonants simultaneously can create system-wide phonological change for children with CLP and severe SSD. Further research with more participants is needed to strengthen these findings.

The use of artificial intelligence and machine learning methods in early pregnancy pre-eclampsia screening: A systematic review protocol.

INTRODUCTION: Pre-eclampsia (PE) is a leading cause of perinatal morbidity and mortality worldwide. Low-dose aspirin can prevent PE in high risk pregnancies if started early. However, despite intense research into the area, early pregnancy screening for PE risk is still not a routine part of pregnancy care. Several studies have described the application of artificial intelligence (AI) and machine learning (ML) in risk prediction of PE and its subtypes. A systematic review of available literature is necessary to catalogue the current applications of AI/ML methods in early pregnancy screening for PE, in order to better inform the development of clinically relevant risk prediction algorithms which will enable timely intervention and the development of new treatment strategies. The aim of this systematic review is to identify and assess studies regarding the application of AI/ML methods in early pregnancy screening for PE. METHODS: A systematic review of peer-reviewed as well as the pre-published cohort, case-control, or cross-sectional studies will be conducted. Relevant information will be accessed from the following databases; PubMed, Google Scholar, Scopus, Embase, Web of Science, Cochrane Library, Arxiv, BioRxiv, and MedRxiv. The studies will be evaluated by two reviewers in a parallel, blind assessment of the literature, a third reviewer will assess any studies in which the first two reviewers did not agree. The free online tool Rayyan, will be used in this literature assessment stage. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist will be used to guide the review process and the methods of the studies will be assessed using the Newcastle-Ottawa scale. Narrative synthesis will be conducted for all included studies. Meta-analysis will also be conducted where data quality and availability allow. ETHICS AND DISSEMINATION: The review will not require ethical approval and the findings will be published in a peer-reviewed journal using the PRISMA guidelines. TRIAL REGISTRATION: Trial registration: The protocol for this systematic review has been registered in PROSPERO [CRD42022345786]. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022345786.

The impact of the COVID-19 lockdown on birthweight among singleton term birth in Denmark.

In Denmark, a nationwide COVID-19 lockdown was implemented on March 12, 2020 and eased on April 14, 2020. The COVID-19 lockdown featured reduced prevalence of extremely preterm or extremely low birthweight births. This study aims to explore the impact of this COVID-19 lockdown on term birthweights in Denmark. We conducted a nationwide register-based cohort study on 27,870 live singleton infants, born at term (weeks 37-41), between March 12 and April 14, 2015-2020, using data from the Danish Neonatal Screening Biobank. Primary outcomes, corrected for confounders, were birthweight, small-for-gestational-age (SGA), and large-for-gestational-age (LGA), comparing the COVID-19 lockdown to the previous five years. Data were analysed using linear regression to assess associations with birthweight. Multinomial logistic regression was used to assess associations with relative-size-for-gestational-age (xGA) categories. Adjusted mean birthweight was significantly increased by 16.9 g (95% CI = 4.1-31.3) during the lockdown period. A dip in mean birthweight was found in gestational weeks 37 and 38 balanced by an increase in weeks 40 and 41. The 2020 lockdown period was associated with an increased LGA prevalence (aOR 1.13, 95% CI = 1.05-1.21). No significant changes in proportions of xGA groups were found between 2015 and 2019. The nationwide COVID-19 lockdown resulted in a small but significant increase in birthweight and proportion of LGA infants, driven by an increase in birthweight in gestational weeks 40 and 41.

Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths.

BACKGROUND: Heart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone. METHODS: We used a newly invented symbolic regression method called the QLattice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified non-linear mathematical transformations of the available covariates, which we then used in a Cox model to predict survival. RESULTS: An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations. CONCLUSION: Symbolic regression is a way to find transformations of covariates from patients' medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.