Effect of dexamethasone on intelligence and hearing in preterm infants: a meta-analysis

OBJECTIVE： A meta-analysis of published randomized controlled trials investigating the long- term effect of dexamethasone on the nervous system of preterm infants. DATA SOURCES： Online literature retrieval was conducted using The Cochrane Library （from January 1993 to June 2013）, EMBASE （from January 1980 to June 2013）, MEDLINE （from Janu- ary 1963 to June 2013）, OVID （from January 1993 to June 2013）, Springer （from January 1994 to June 2013） and Chinese Academic Journal Full-text Database （from January 1994 to June 2013）. Key words were preterm infants and dexamethasone in English and Chinese. STUDY SELECTION： Selected studies were randomized controlled trials assessing the effect of intravenous dexamethasone in preterm infants. The quality of the included papers was evaluated and those without the development of the nervous system and animal experiments were exclud- ed. Quality assessment was performed through bias risk evaluation in accordance with Cochrane Handbook 5.1.0 software in the Cochrane Collaboration. The homogeneous studies were analyzed and compared using Revman 5.2.6 software, and then effect model was selected and analyzed. Those papers failed to be included in the meta-analysis were subjected to descriptive analysis. MAIN OUTCOME MEASURES： Nervous system injury in preterm infants. RESULTS： Ten randomized controlled trials were screened, involving 1,038 subjects. Among them 512 cases received dexamethasone treatment while 526 cases served as placebo control group and blank control group. Meta-analysis results showed that the incidence of cerebral palsy, visual im- pairment and hearing loss in preterm infants after dexamethasone treatment within 7 days after birth was similar to that in the control group （RR = 1.47, 95%CI： 0.97-2.21; RR = 1.46, 95%CI： 0.97-2.20; RR = 0.80, 95%CI： 0.54-1.18; P 〉 0.05）, but intelligence quotient was significantly de- creased compared with the control group （MD = -3.55, 95%CI： -6.59 to -0.51; P = 0.02）. Prete rm infants treated with dexamethasone 7 days after birth demonstrated an incidence of cerebral palsy and visual impairment, and changes in intelligence quotient similar to those in the control group （RR = 1.26, 95%CI： 0.89-1.79; RR = 1.37, 95%CI： 0.73-2.59; RR = 0.53, 95%CI： 0.32-0.89; RR = 1.66, 95%CI： -4.7 to 8.01; P 〉 0.05）. However, the incidence of hearing loss was significantly increased compared with that in the control group （RR = 0.53, 95%CI： 0.32-0.89; P = 0.02）. CONCLUSION： Dexamethasone may affect the intelligence of preterm infants in the early stages after birth, but may lead to hearing impairment at later stages after birth. More reliable conclusions should be made through large-size, multi-center, well-designed randomized controlled trials.

Public Reaction to Scientific Research via Twitter Sentiment Prediction

Purpose:Social media users share their ideas,thoughts,and emotions with other users.However,it is not clear how online users would respond to new re search outcomes.This study aims to predict the nature of the emotions expressed by Twitter users toward scientific publications.Additionally,we investigate what features of the research articles help in such prediction.Identifying the sentiments of research articles on social media will help scientists gauge a new societal impact of their research articles.Design/methodology/appro ach:Several tools are used for sentiment analysis,so we applied five sentiment analysis tools to check which are suitable for capturing a tweet’s sentiment value and decided to use NLTK VADER and TextBlob.We segregated the sentiment value into negative,positive,and neutral.We measure the mean and median of tweets’sentiment value for research articles with more than one tweet.We next built machine learning models to predict the sentiments of tweets related to scientific publications and investigated the essential features that controlled the prediction models.Findings:We found that the most important feature in all the models was the sentiment of the research article title followed by the author count.We observed that the tree-based models performed better than other classification models,with Random Forest achieving 89%accuracy for binary clas sification and 73%accuracy for three-label clas sification.Research limitations:In this research,we used state-of-the-art sentiment analysis libraries.However,these libraries might vary at times in their sentiment prediction behavior.Tweet sentiment may be influenced by a multitude of circumstances and is not always immediately tied to the paper’s details.In the future,we intend to broaden the scope of our research by employing word2 vec models.Practical implications:Many studies have focused on understanding the impact of science on scientists or how science communicators can improve their outcomes.Research in this area has relied on fewer and more limited measures,such as citations and user studies with small datasets.There is currently a critical need to find novel methods to quantify and evaluate the broader impact of research.This study will help scientists better comprehend the emotional impact of their work.Additionally,the value of understanding the public’s interest and reactions helps science communicators identify effective ways to engage with the public and build positive connections between scientific communities and the public.Originality/value:This study will extend work on public engagement with science,sociology of science,and computational social science.It will enable researchers to identify areas in which there is a gap between public and expert understanding and provide strategies by which this gap can be bridged.