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IntroductionThe prevalence of both obesity and gestational diabetes mellitus (GDM) has increased, and each is associated with adverse perinatal outcomes including fetal overgrowth, neonatal morbidity, hypertensive disorders of pregnancy and caesarean delivery. Women with GDM who are also overweight or obese have higher rates of pregnancy complications when compared with normal-weight women with GDM, which may occur in part due to suboptimal glycaemic control. The current recommendations for glycaemic targets in pregnant women with diabetes are based on limited evidence and exceed the mean fasting (70.9±7.8 mg/dL) and 1-hour postprandial (108.9±12.9 mg/dL) glucose values in pregnant individuals without diabetes. Our prior work demonstrated that the use of intensive (fasting <90 mg/dL and 1-hour postprandial <120 mg/dL) compared with standard (fasting <95 mg/dL and 1-hour postprandial <140 mg/dL) glycaemic targets resulted in improved glycaemic control without increasing the risk for hypoglycaemia in pregnant individuals with GDM, but the impact of intensive glycaemic targets on perinatal outcomes is unknown.Methods and analysisThe Intensive Glycemic Targets in Overweight and Obese Women with Gestational Diabetes Mellitus: A Multicenter Randomized Trial (iGDM Trial) is a large, pragmatic randomised clinical trial designed to investigate the impact of intensive versus standard glycaemic targets on perinatal outcomes in women with GDM who are overweight and obese. During the 5-year project period, a multidisciplinary team of investigators from five medical centres representing regions of the USA with high rates of obesity will randomise 828 overweight and obese women with GDM to either intensive or standard glycaemic targets. We will test the central hypothesis that intensive glycaemic targets will result in lower rates of neonatal composite morbidity including large for gestational age birth weight, neonatal hypoglycaemia, respiratory distress syndrome and need for phototherapy when compared with standard glycaemic targets using the intention-to-treat approach to analysis.Ethics and disseminationThe Institutional Review Board (IRB) at Indiana University School of Medicine approved this study (IRB# 11435; initial approval date 25 August 2021). We will submit the results of the trial for publication in peer-reviewed journals and presentations at international scientific meetings.Trial registration numberNCT05124808.

The aim was to assess the precision and accuracy of cephalometric analyses performed by artificial intelligence (AI) with and without human augmentation. Four dental professionals with varying experience levels identified 31 landmarks on 30 cephalometric radiographs twice. These landmarks were re-identified by all examiners with the aid of AI. Precision and accuracy were assessed by using intraclass correlation coefficients (ICCs) and mean absolute errors (MAEs). AI revealed the highest precision, with a mean ICC of 0.97, while the dental student had the lowest (mean ICC: 0.77). The AI/human augmentation method significantly improved the precision of the orthodontist, resident, dentist, and dental student by 3.26%, 2.17%, 19.75%, and 23.38%, respectively. The orthodontist demonstrated the highest accuracy with an MAE of 1.57 mm/°. The AI/human augmentation method improved the accuracy of the orthodontist, resident, dentist, and dental student by 12.74%, 19.10%, 35.69%, and 33.96%, respectively. AI demonstrated excellent precision and good accuracy in automated cephalometric analysis. The precision and accuracy of the examiners with the aid of AI improved by 10.47% and 27.27%, respectively. The AI/human augmentation method significantly improved the precision and accuracy of less experienced dental professionals to the level of an experienced orthodontist.

In the field of orthodontics, providing patients with accurate treatment time estimates is of utmost importance. As orthodontic practices continue to evolve and embrace new advancements, incorporating machine learning (ML) methods becomes increasingly valuable in improving orthodontic diagnosis and treatment planning. This study aimed to develop a novel ML model capable of predicting the orthodontic treatment duration based on essential pre-treatment variables. Patients who completed comprehensive orthodontic treatment at the Indiana University School of Dentistry were included in this retrospective study. Fifty-seven pre-treatment variables were collected and used to train and test nine different ML models. The performance of each model was assessed using descriptive statistics, intraclass correlation coefficients, and one-way analysis of variance tests. Random Forest, Lasso, and Elastic Net were found to be the most accurate, with a mean absolute error of 7.27 months in predicting treatment duration. Extraction decision, COVID, intermaxillary relationship, lower incisor position, and additional appliances were identified as important predictors of treatment duration. Overall, this study demonstrates the potential of ML in predicting orthodontic treatment duration using pre-treatment variables.

Background The exposure of young adults to electronic cigarette (e-cigarette) advertisements has risen rapidly. E-cigarette ads have been shown to increase short term perceived acceptability of using e-cigarettes in places where traditional cigarettes are banned. We set out to investigate if advertising exposure was related to perceptions of harm, addictiveness, and acceptability of use of e-cigarettes in places where traditional cigarettes are banned. Methods Using a cross-sectional design, 6037 students at a large Midwestern university between the ages of 18–24 were surveyed about e-cigarette use and smoking status. Bivariate analyses were performed associating perception of harm, addictiveness, and acceptability of e-cigarette use in places where smoking is banned with demographic and other background factors, and e-cigarette advertising exposure through different media channels. Logistic regression analyses were used to explore the relationship of these factors on perceptions of harm, addictiveness and acceptability of e-cigarette use in places where smoking is banned. Results More than a quarter (27.4%) of respondents had used an e-cigarette, greater than half (53.2%) had seen an advertisement on TV and 42.0% had seen an advertisement on the Internet. Logistic regressions revealed that being white, male, an e-cigarette user, a smoker, having a mother who smoked, and Internet advertisement exposure were associated with lower perceived harm of e-cigarettes. The same factors, plus having seen advertisements on TV, were associated with increased likelihood of perceiving e-cigarette use in bars, stores, at work and in a dorm as acceptable. Perceiving use of e-cigarettes as acceptable in classrooms was also associated with the aforementioned factors and also included race. Only being male and an e-cigarette user were associated with lower perceived addictiveness of e-cigarettes. Conclusions E-cigarette use is increasing in adolescents and young adults, as is exposure to e-cigarette advertising. Independent of nicotine use and demographics factors, e-cigarette advertising is associated with increased beliefs in acceptability of e-cigarette use in places where cigarettes are banned. E-cigarette advertisements may be responsible for normalizing e-cigarette use. Exposure to internet e-cigarette advertisements was associated with lower perceived harm; this may be due to the false health claims often made in internet advertisements.

Objectives:The main objective of this study was to report stress and anxiety levels during the COVID-19 pandemic on early- to mid-career women researchers in healthcare sciences research and determine the associated factors.Methods:A 50-item self-administered internet questionnaire was developed using a mix of Likert-type scales and open-ended response questions. The survey was distributed June 10–August 3, 2020. Anxiety and stress as well as personal/family demands were assessed through validated measures (Patient Reported Outcomes Measurement Information System [PROMIS]-Anxiety Short Form and Perceived Stress Scale [PSS]) and open-ended responses.Results:One hundred and fifty-one early-career women in healthcare sciences research completed the survey; mean respondent age was 37.3 ± 5.2 years; and all had a college degree or higher, 50.3% holding a PhD and 35.8% MD. Race and ethnicity were reported in 128; the majority were White (74.0%). One-third (31.2%) reported being “very much” concerned about reaching their research productivity goals and 30.1% were “very much” concerned about academic promotion and tenure. Fifty percent reported a “moderate” PROMIS anxiety score and 72.1% reported a “moderate” PSS score. For the open-ended responses, 65.6% reported a worry about their professional goals because of the COVID-19 pandemic. Major concerns revolved around finances, childcare, and job security.Conclusions:Throughout the pandemic, early- and mid-career women in healthcare sciences research have reported moderate to high overall stress, anxiety, and worries. These concerns appear related to household settings, additional responsibilities, financial concerns, and reduced research productivity. Institutions and funding agencies should take these concerns into consideration and offer support.

ObjectiveBreastfeeding has multiple benefits for women and babies. Understanding factors contributing to intention to exclusively breastfeed may allow for improving the rates in first-time mothers. The study objective was to examine factors associated with a woman’s intention to breastfeed her first child.MethodsA secondary analysis of the prospective “Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be” (nuMoM2b) study of nulliparous women in the U.S. with singleton pregnancies was performed. Sociodemographic and psychosocial factors were analyzed for associations with breastfeeding intention.ResultsFor the 6443 women with complete information about breastfeeding intention and all factors under consideration, women who intended to breastfeed (either exclusively or any breastfeeding) were more likely to be older, not black, have reached a higher level of education, have higher incomes, have a lower body mass index (BMI), and be nonsmokers. Reporting a planned pregnancy and several psychosocial measures were also associated with intention to breastfeed. In the multivariable analysis for exclusive breastfeeding, in addition to age, BMI, race, income, education, and smoking, of the psychosocial measures assessed, only women with higher hassle intensity ratios on the Pregnancy Experience Scale had lower odds of exclusive breastfeeding intention (OR 0.71, 95% CI 0.55–0.92). Other psychosocial measures were not associated with either exclusive breastfeeding or any breastfeeding after controlling for demographic characteristics.Conclusions for PracticeSeveral sociodemographic factors, having a planned pregnancy, and fewer intense pregnancy hassles compared to uplifts are associated with intention to exclusively breastfeed. Identifying these factors may allow providers to identify women for focused, multilevel efforts to enhance breastfeeding rates.

PurposeTo assess the relationship between decision quality and mental health outcomes for women and their important others (IO) 3 months following periviable birth.MethodMental health outcomes were assessed prior to delivery and at 3 months postpartum using depression (PHQ-9), anxiety (GAD-7), and post-traumatic stress disorder (PTSD) (IES-22) scales. Decision quality was measured in terms of Decisional Conflict, Control, Regret, and Satisfaction with Decision. Descriptive analyses and linear regression modeling were conducted using SAS version 9.4.ResultWe recruited 30 eligible women and 16 IOs. Participants had mild anxiety and depression, and symptoms of PTSD were among bereaved parents. Participants with lower decision control had higher levels of depression (women: p = 0.014; IOs: p = 0.059) and anxiety (women: p = 0.053; IOs: p = 0.032). Depression was also associated with higher decisional regret (women: p = 0.073; IOs: p = 0.023).ConclusionOur findings suggest that decision quality is associated with mental health outcomes for families who experience periviable delivery.

Symptoms of emotional distress during and after pregnancy may be introduced or exacerbated by unexpected medical conditions in the mother or fetus. Twin-twin transfusion syndrome (TTTS), which accounts for 17% of fetal deaths in twins and entails substantial medical uncertainty, may represent a particularly challenging pregnancy experience. Yet, little is known about the impact of TTTS on women’s emotional health. We retrospectively surveyed 350 women who experienced a TTTS pregnancy about their experiences at three time points (prior to, during, and after pregnancy) to examine symptoms of anxiety and depression, mental health diagnoses, thoughts of seeking mental healthcare, help received, and preferred mental health services. Women in this study experienced significantly elevated symptoms of depression and anxiety during and after pregnancy, regardless of their pregnancy outcome (double survivor, single survivor, or double loss). Women reported feeling devastated by their experience and indicated they would have accepted mental healthcare had it been offered and had barriers to care been addressed. Prospective studies of women experiencing TTTS pregnancies are needed to examine TTTS effects on maternal mental health and to determine how to best address emotional care needs.