The COVID-19 pandemic has affected the entire world, resulting in a tremendous change to people’s lifestyles. We investigated the Korean public response to COVID-19 vaccines on social media from 23 February 2021 to 22 March 2021. We collected tweets related to COVID-19 vaccines using the Korean words for “coronavirus” and “vaccines” as keywords. A topic analysis was performed to interpret and classify the tweets, and a sentiment analysis was conducted to analyze public emotions displayed within the retrieved tweets. Out of a total of 13,414 tweets, 3509 were analyzed after preprocessing. Eight topics were extracted using the Latent Dirichlet Allocation model, and the most frequently tweeted topic was vaccine hesitation, consisting of fear, flu, safety of vaccination, time course, and degree of symptoms. The sentiment analysis revealed a similar ratio of positive and negative tweets immediately before and after the commencement of vaccinations, but negative tweets were prominent after the increase in the number of confirmed COVID-19 cases. The public’s anticipation, disappointment, and fear regarding vaccinations are considered to be reflected in the tweets. However, long-term trend analysis will be needed in the future.

Background and Objectives: Chronic lower back pain (LBP) is a common clinical disorder. The early identification of patients who will develop chronic LBP would help develop preventive measures and treatment. We aimed to develop machine learning models that can accurately predict the risk of chronic LBP. Materials and Methods: Data from the Sixth Korea National Health and Nutrition Examination Survey conducted in 2014 and 2015 (KNHANES VI-2, 3) were screened for selecting patients with chronic LBP. LBP lasting >30 days in the past 3 months was defined as chronic LBP in the survey. The following classification models with machine learning algorithms were developed and validated to predict chronic LBP: logistic regression (LR), k-nearest neighbors (KNN), naïve Bayes (NB), decision tree (DT), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and artificial neural network (ANN). The performance of these models was compared with respect to the area under the receiver operating characteristic curve (AUROC). Results: A total of 6119 patients were analyzed in this study, of which 1394 had LBP. The feature selected data consisted of 13 variables. The LR, KNN, NB, DT, RF, GBM, SVM, and ANN models showed performances (in terms of AUROCs) of 0.656, 0.656, 0.712, 0.671, 0.699, 0.660, 0.707, and 0.716, respectively, with ten-fold cross-validation. Conclusions: In this study, the ANN model was identified as the best machine learning classification model for predicting the occurrence of chronic LBP. Therefore, machine learning could be effectively applied in the identification of populations at high risk of chronic LBP.

Background: The usefulness of the oxygen reserve index (ORi) in reducing hyperoxemia remains unclear. We designed this study to investigate whether fraction of inspired oxygen (FiO₂) adjustment under a combination of ORi and peripheral oxygen saturation (SpO₂) guidance can reduce intraoperative hyperoxemia compared to SpO₂ alone. Methods: In this prospective, double-blind, randomized controlled study, we allocated patients scheduled for laparoscopic gastrectomy to the SpO₂ group (FiO₂ adjusted to target SpO₂ ≥ 98%) or the ORi-SpO₂ group (FiO₂ adjusted to target 0 < 0 ORi < .3 and SpO₂ ≥ 98%). The ORi, SpO₂, FiO₂, arterial partial pressure of oxygen (PaO₂), and incidence of severe hyperoxemia (PaO_{2 ≥} 200 mm Hg) were recorded before and 1, 2, and 3 hours after surgical incision. Data from 32 and 30 subjects in the SpO₂ and ORi-SpO₂ groups, respectively, were analyzed. Results: PaO₂ was higher in the SpO₂ group (250.31 ± 57.39 mm Hg) than in the ORi-SpO₂ group (170.07 ± 49.39 mm Hg) 1 hour after incision (P < .001). PaO₂ was consistently higher in the SpO₂ group than in the ORi-SpO₂ group, over time (P = .045). The incidence of severe hyperoxemia was higher in the SpO₂ group (84.4%) than in the ORi-SpO₂ group (16.7%, P < .001) 1 hour after incision. Higher FiO₂ was administered to the SpO₂ group [52.5 (50–60)] than the ORi-SpO₂ group [40 (35–50), P < .001] 1 hour after incision. SpO₂ was not different between the 2 groups. Conclusion: The combination of ORi and SpO₂ guided FiO₂ adjustment reduced hyperoxemia compared to SpO₂ alone during laparoscopic gastrectomy.