Background The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic. Objective The aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19. Methods This study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19–related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted. Results There were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2%) tweets as having a positive sentiment, 187,042 (20.7%) as neutral, and 280,842 (31.1%) as negative. The study identified 5 dominant themes among COVID-19–related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee, and North Carolina conveyed the most positive sentiment. Conclusions This study identified 5 prevalent themes of COVID-19 discussion with sentiments ranging from positive to negative. These themes and sentiments can clarify the public’s response to COVID-19 and help officials navigate the pandemic.
Objective This study sought to utilise machine learning methods in artificial intelligence to select the most relevant variables in classifying the presence and absence of root caries and to evaluate the model performance. Background Dental caries is one of the most prevalent oral health problems. Artificial intelligence can be used to develop models for identification of root caries risk and to gain valuable insights, but it has not been applied in dentistry. Accurately identifying root caries may guide treatment decisions, leading to better oral health outcomes. Methods Data were obtained from the 2015‐2016 National Health and Nutrition Examination Survey and were randomly divided into training and test sets. Several supervised machine learning methods were applied to construct a tool that was capable of classifying variables into the presence and absence of root caries. Accuracy, sensitivity, specificity and area under the receiver operating curve were computed. Results Of the machine learning algorithms developed, support vector machine demonstrated the best performance with an accuracy of 97.1%, precision of 95.1%, sensitivity of 99.6% and specificity of 94.3% for identifying root caries. The area under the curve was 0.997. Age was the feature most strongly associated with root caries. Conclusion The machine learning algorithms developed in this study perform well and allow for clinical implementation and utilisation by dental and nondental professionals. Clinicians are encouraged to adopt the algorithms from this study for early intervention and treatment of root caries for the ageing population of the United States, and for attaining precision dental medicine.
Evolutionary biology has yet to reconcile the ubiquity of sex with its costs relative to asexual reproduction. Here, we test the hypothesis that coevolving parasites maintain sex in their hosts. Specifically, we examined the distributions of sexual reproduction and susceptibility to local parasites within a single population of freshwater snails (Potamopyrgus antipodarum). Susceptibility to local trematode parasites (Microphallus sp.) is a relative measure of the strength of coevolutionary selection in this system. Thus, if coevolving parasites maintain sex, sexual snails should be common where susceptibility is high. We tested this prediction in a mixed population of sexual and asexual snails by measuring the susceptibility of snails from multiple sites in a lake. Consistent with the prediction, the frequency of sexual snails was tightly and positively correlated with susceptibility to local parasites. Strikingly, in just two years, asexual females increased in frequency at sites where susceptibility declined. We also found that the frequency of sexual females covaries more strongly with susceptibility than with the prevalence of Microphallus infection in the field. In linking susceptibility to the frequency of sexual hosts, our results directly implicate spatial variation in coevolutionary selection in driving the geographic mosaic of sex.
Study Design. This was a correlational study. Objective. Determine the range of pediatric Patient-Reported Outcomes Measurement Information System (PROMIS) scores for patients treated for adolescent idiopathic scoliosis (AIS) and assess correlation with Scoliosis Research Society-22 (SRS-22) domain scores. Summary of Background Data. Patient reported outcome (PRO) measures are important metrics for measuring health status in diverse patient populations. PROMIS is increasingly being used in orthopedic practice. Existing literature compares PROMIS measures favorably to legacy measures in numerous adult orthopedic conditions. This study sought to define the range of PROMIS mobility, pain interference, and peer relationships scores for adolescents treated for AIS. Furthermore, correlations between these domains and equivalent domains in the legacy PRO, SRS-22, were determined. Methods. Pediatric PROMIS and SRS-22 were obtained at routine clinical visits for AIS at a tertiary care children's hospital from January 2017 to October 2017. Spearman correlations were performed to examine the associations between three pediatric PROMIS domains and the SRS-22 domains. Only patients who completed both PRO measures were included in the analyses. Radiographic measurements were performed at each visit assessing sagittal and coronal deformity and overall spinal balance. Results. One hundred thirteen patients with a mean age of 14.4 (standard deviation [SD] = 2.1) years completed the assessments. The mean pediatric PROMIS domain scores included: mobility 50.9 (interquartile range [IQR] 36.2–65.6); pain interference 45.9 (IQR 28.9–62.9); peer relations 52.6 (IQR 38.3–64.9). PROMIS mobility was strongly correlated with SRS-22 function (r = 0.65; P < 0.001). PROMIS pain interference was strongly correlated with SRS-22 pain (r = 0.70; P < 0.001). PROMIS peer relations was moderately correlated with SRS-22 Mental Health (r = 0.41; P < 0.001) and self-image (r = 0.34; P < 0.001). Conclusion. In AIS patients pediatric PROMIS pain interference and mobility correlate strongly with SRS-22 pain and function domains, while PROMIS peer relationships demonstrates moderate correlations with SRS-22 mental health and self-image. Level of Evidence: 2
Introduction As total health and dental care expenditures in the United States continue to rise, healthcare disparities for low to middle-income Americans creates an imperative to analyze existing expenditures. This study examined health and dental care expenditures in the United States from 1996 to 2016 and explored trends in spending across various population subgroups. Methods Using data collected by the Medical Expenditure Panel Survey, this study examined health and dental care expenditures in the United States from 1996 to 2016. Trends in spending were displayed graphically and spending across subgroups examined. All expenditures were adjusted for inflation or deflation to the 2016 dollar. Results Both total health and dental expenditures increased between 1996 and 2016 with total healthcare expenditures increasing from $838.33 billion in 1996 to $1.62 trillion in 2016, a 1.9-fold increase. Despite an overall increase, total expenditures slowed between 2004 and 2012 with the exception of the older adult population. Over the study period, expenditures increased across all groups with the greatest increases seen in older adult health and dental care. The per capita geriatric dental care expenditure increased 59% while the per capita geriatric healthcare expenditure increased 50% across the two decades. For the overall US population, the per capita dental care expenditure increased 27% while the per capita healthcare expenditure increased 60% over the two decades. All groups except the uninsured experienced increased dental care expenditure over the study period.
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