Resumo em Inglês:

SUMMARY OBJECTIVE: The aim of the study was to compare two labor induction regimens (4 and 6 h), to determine predictors of successful labor induction with intravaginal misoprostol 25 μg tablets, and to evaluate the association with adverse perinatal outcomes. METHODS: This was a retrospective cohort study that included singleton pregnancies undergoing induction of labor with an intravaginal misoprostol 25 μg tablet between 37 and 42 weeks of gestation. The pregnant women were divided into two groups: Group 1—intravaginal misoprostol 25 μg every 4 h and Group 2—intravaginal misoprostol 25 μg every 6 h. RESULTS: Pregnant women were divided into Group 1 (n=289) and Group 2 (n=278). Group 1 had a higher median number of intravaginal misoprostol 25 μg tablets (3.0 vs. 2.0 tablets, p<0.001), a lower prevalence of postpartum hemorrhage (7.6 vs. 32.7%, p<0.001), and a higher need for oxytocin (odds ratio [OR]: 2.1, 95%CI: 1.47–2.98, p<0.001) than Group 2. Models including intravaginal misoprostol 25 μg tablets every 4 and 6 h [x2(1)=23.7, OR: 4.35, p<0.0001], parity [x2(3)=39.4, OR: 0.59, p=0.031], and Bishop’s score [x2(4)=10.8, OR: 0.77, p=0.019] were the best predictors of failure of labor induction. A statistically significant difference between groups was observed between the use of the first intravaginal misoprostol 25 μg tablet at the beginning (Breslow p<0.001) and the end of the active labor phase (Long Hank p=0.002). CONCLUSION: Pregnant women who used intravaginal misoprostol 25 μg every 4 h had a longer time from the labor induction to the beginning of the active phase of labor and higher rates of adverse perinatal outcomes than women who used intravaginal misoprostol 25 μg every 6 h.

Resumo em Inglês:

SUMMARY OBJECTIVE: The primary objective was to assess the diagnostic accuracy of a deep learning-based artificial intelligence model for the detection of acute appendicular fractures in pediatric patients presenting with a recent history of trauma to the emergency department. The secondary goal was to examine the effect of assistive support on the emergency doctor’s ability to detect fractures. METHODS: The dataset was 5,150 radiographs of which 850 showed fractures, while 4,300 radiographs did not show any fractures. The process utilized 4,532 (88%) radiographs, inclusive of both fractured and non-fractured radiographs, in the training phase. Subsequently, 412 (8%) radiographs were appraised during validation, and 206 (4%) were set apart for the testing phase. With and without artificial intelligence assistance, the emergency doctor reviewed another set of 2,000 radiographs (400 fractures and 600 non-fractures each) for labeling in the second test. RESULTS: The artificial intelligence model showed a mean average precision 50 of 89%, a specificity of 92%, a sensitivity of 90%, and an F1 score of 90%. The confusion matrix revealed that the model trained with artificial intelligence achieved accuracies of 93 and 95% in detecting fractures, respectively. Artificial intelligence assistance improved the reading sensitivity from 93.7% (without assistance) to 97.0% (with assistance) and the reading accuracy from 88% (without assistance) to 94.9% (with assistance). CONCLUSION: A deep learning-based artificial intelligence model has proven to be highly effective in detecting fractures in pediatric patients, enhancing the diagnostic capabilities of emergency doctors through assistive support.

Resumo em Inglês:

SUMMARY OBJECTIVE: With the spread of smartphones, they have become an indispensable part of life, and nomophobia (No-Mobile-Phone Phobia) has emerged. METHODS: The present research is a cross-sectional study and was conducted with 3,870 primiparous pregnant women between April and May 2022. The research data were collected using the Personal Information Form, Nomophobia Questionnaire, and Wijma Delivery Expectancy/Experience Questionnaire. RESULTS: The Wijma Delivery Expectancy/Experience Questionnaire score of the pregnant women who participated in the study was 22.3% (n=863) had a clinical fear of childbirth and 19.5% (n=753) had extreme nomophobia. Considering the correlation of the Nomophobia Questionnaire and Wijma Delivery Expectancy/Experience Questionnaire scores with other variables, it was found that the Wijma Delivery Expectancy/Experience Questionnaire scores increased with the increasing Nomophobia Questionnaire total score (p=0.000, r=236) and the Nomophobia Questionnaire total score and fear of childbirth increased with an increase in the daily phone usage time. It was also revealed that women who had smartphone applications related to fetal development had higher nomophobia levels (p=0.0001), while they had a lower fear of childbirth. CONCLUSION: This study found that one in every five pregnant women was extremely nomophobic and had a clinical fear of childbirth and that nomophobia and the fear of childbirth were correlated at the clinical level. In this regard, women should prefer face-to-face communication rather than smartphones throughout the pregnancy period.