ABSTRACT
In counterfactual thinking, an imagined alternative to the reality that comprises an antecedent and a consequent is widely adopted in political discourse to justify past behaviors (i.e., counterfactual explanation) or to depict a better future (i.e., prefactual). However, they have not been properly addressed in political communication literature. Our study examines how politicians used counterfactual expressions for explanation of the past or preparation of the future during COVID-19, one of the most severe public health crises. All Congressional speeches of the Senate and House in the 116th Congress (2019-2020) were retrieved, and counterfactual expressions were identified along with time-focusing in each speech, using recent advances in natural language processing (NLP) techniques. The results show that counterfactuals were more practiced among Democrats in the Senate and Republicans in the House. With the spread of the pandemic, the use of counterfactuals decreased, maintaining a partisan gap in the House. However, it was nearly stable, with no party differences in the Senate. Implications of our findings are discussed, regarding party polarization, institutional constraints, and the quality of Congressional deliberation. Limitations and suggestions for future research are also provided.
ABSTRACT
Background: Obesity is a bona fide risk factor for ICU admission, mechanical ventilation, and mortality in patients (pts) with COVID-19 in the general population. However, whether obesity is a risk factor in cancer pts remains unknown. Herein, we have conducted a systematic review/meta-analysis of obesity and all-cause mortality in cancer pts with COVID-19. Methods: Following PRISMA guidelines,a systematic search of PubMed and Embase as well as major conference proceedings (ASCO/ESMO/AACR) was conducted for publications from inception to 14 January 2020. Observational studies that reported all-cause mortality in cancer pts with lab confirmation or clinical diagnosis of COVID19 and BMI (obese (>30 kg/m2 ) vs. non-obese) were included in the analysis. The pooled odds ratio (OR) and 95% confidence interval (CI) were calculated with the fixed-effects model based on low heterogeneity. Small sample publication bias was evaluated using the Begg's Funnel Plot and Egger's test. Results: After reviewing 3387 studies,3 retrospective cohort studies of 419 obese and 1694 non-obese cancer pts (N=2117) with COVID-19 in both inpatient/outpatient settings that reported outcomes based on obesity were found. The 3 studies were conducted multi-nationally in North America, in France, and in the Netherlands respectively. The median ages of the cohorts ranged 66-68. All studies included various cancers of various stages and were of high quality per Newcastle Ottawa scale (scores 7-9). Fixed effects metaanalysis showed no association between obesity and all-cause mortality (OR 0.95, 95% CI 0.74- 1.23) in cancer pts with COVID-19. Heterogeneity was low (I2= 33%). No significant funnel plot asymmetry was detected per Egger's test (P=0.2273). The reported OR of each study is outlined in the table. Conclusions: In contrast to the general population, our analysis reveals that obesity is not associated with increased all-cause mortality in cancer pts with COVID-19. Limitations of this study include a limited number of included studies, reliance on retrospective studies, non-use of ethnicityspecific WHO BMI criteria, and limited granularity of the study-reported BMI. Future prospective studies are warranted to assess the complex interplay among anthropomorphic measures, cachexia/sarcopenia, comorbidities associated with the metabolic syndrome, and COVID-19 outcomes in the cancer pt population. (Table Presented).
ABSTRACT
With the advancement of Artificial Intelligence technology, the development of various applied software and studies are actively conducted on detection, classification, and prediction through interdisciplinary convergence and integration. Among them, medical AI has been drawing huge interest and popularity in Computer-Aided Diagnosis, which collects human body signals to predict abnormal symptoms of health, and diagnoses diseases through medical images such as X-ray and CT. Since X-ray and CT in medicine use high-resolution images, they require high specification equipment and huge energy consumption due to high computation in learning and recognition, incurring huge costs to create an environment for operation. Thus, this paper proposes a chest X-ray outlier detection model using dimension reduction and edge detection to solve these issues. The proposed method scans an X-ray image using a window of a certain size, conducts difference imaging of adjacent segment-images, and extracts the edge information in a binary format through the AND operation. To convert the extracted edge, which is visual information, into a series of lines, it is computed in convolution with the detection filter that has a coefficient of 2n and the lines are divided into 16 types. By counting the converted data, a one-dimensional 16-size array per one segment-image is produced, and this reduced data is used as an input to the RNN-based learning model. In addition, the study conducted various experiments based on the COVID-chest X-ray dataset to evaluate the performance of the proposed model. According to the experiment results, the LFA-RNN showed the highest accuracy at 97.5% in the learning calculated through learning, followed by CRNN 96.1%, VGG 96.6%, AlexNet 94.1%, Conv1D 79.4%, and DNN 78.9%. In addition, LFA-RNN showed the lowest loss at about 0.0357. CCBY
ABSTRACT
Importance: There is strong evidence that COVID-19 is associated with higher morbidity and mortality in malescompared to females in the general population. However, whether the same sex bias exists in the cancer patientpopulation is unknown. Several published studies have examined this question, but the results are inconclusive andinconsistent and the association remains unclear. Objective: To evaluate the sex differences in the risk of severe illness and mortality attributable to COVID-19 in thecancer patient population. Data Sources: Published articles that evaluated clinical outcomes associated with severe illness or deathattributable to COVID-19 in the cancer patient population from inception to June 1, 2020, were identified bysearching PubMed and EMBASE, as well as the ASCO 2020 Virtual Annual Conference, ESMO conferences heldfrom January 2020 to June 1, 2020, and the preprint databases medRxiv and bioRxiv. Study Selection: Prospective or retrospective analyses, studies published in English, providing clinical outcomesdata with sex differences in the cancer patient population. Data Extraction and Synthesis: Author, date of publication, country, type of studies, median and range of age, cancer types included in the studies, definitions of clinical outcomes, and the odds ratios (OR) for severe illness ordeath attributable to COVID-19 were retrieved. Where OR data were not available, raw data were used to calculatethe OR in a univariate analysis model and included in the meta-analysis. Main Outcome(s) and Measure(s): The primary outcome of interest was OR of (1) severe illness, (2) death, and(3) composite outcome of severe illness and death attributable to COVID-19 in males versus females. Results: Overall, 2,764 patients (9 studies) were analyzed in retrospective study settings. Of the included studies, two studies were multinational whereas the rest were conducted in China (4), France (1), United Kingdom (1), andUnited States (1). Median ages were similar across studies (range 62-70). Three studies reported outcomes fordeath and six studies reported outcomes for severe illness. Of the seven studies, all but one defined severe illnessas illness requiring ICU admission or leading to death and attributable to COVID-19. Pooled ORs for the compositeoutcome was 1.68 (95% CI, 1.27-2.24), death was 1.98 (95% CI, 1.21-3.26), and severe illness was 1.48 (95% CI,1.05-2.10), all disfavoring males. Random effects model was used with the Dersimonian-Laird Model throughoutanalyses and significant heterogeneity was subsequently confirmed (I2, 48.1%;tau2, 0.0816). No significantbetween-study bias was detected per Begg's funnel plot. Conclusions and Relevance: The male sex was associated with higher risk of severe illness, death, and thecomposite outcome of both attributable to COVID-19. This finding has implications in informing the clinical prognosisand decision making regarding oncologic patients.