Physical, Psychological, and Emotional Causality

Previous studies have suggested that adults and infants learn about causal relationships through Bayesian structure learning rather than through associative learning(e.g., Griffiths, Sobel, Tenenbaum, & Gopnik, 2011; Sobel, Tenenbaum, & Gopnik, 2004). This view ostensibly garners support from research that has used a blicket detector, which is a machine that lights up and plays music when certain objects are placed on it (e.g., Sobel et al., 2004; Sobel & Kirkham, 2006). Although a large database exists on physical causal inference, there is a dearth of causality research in other domains, such as psychological and emotional causality, particularly among adult populations. Because little research on causal inference has been conducted with adults in a blicket-detector-like context, this study investigated whether adults reason about causal events through Bayesian inference or associative learning and whether adults are capable of making physical, psychological, and emotional causal inference in a blicket detector paradigm. The results supported the hypothesis that adults are able to make physical, psychological, and emotional causal inference but failed to support the hypothesis that adults use associative learning to reason about causal relationships and showed that adults use Bayesian structure learning

Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study

Background: Suspicion of lesions and prediction of the histology of esophageal cancers or premalignant lesions in endoscopic images are not yet accurate. The local feature selection and optimization functions of the model enabled an accurate analysis of images in deep learning. Objectives: To establish a deep-learning model to diagnose esophageal cancers, precursor lesions, and non-neoplasms using endoscopic images. Additionally, a nationwide prospective multicenter performance verification was conducted to confirm the possibility of real-clinic application. Methods: A total of 5162 white-light endoscopic images were used for the training and internal test of the model classifying esophageal cancers, dysplasias, and non-neoplasms. A no-code deep-learning tool was used for the establishment of the deep-learning model. Prospective multicenter external tests using 836 novel images from five hospitals were conducted. The primary performance metric was the external-test accuracy. An attention map was generated and analyzed to gain the explainability. Results: The established model reached 95.6% (95% confidence interval: 94.2–97.0%) internal-test accuracy (precision: 78.0%, recall: 93.9%, F1 score: 85.2%). Regarding the external tests, the accuracy ranged from 90.0% to 95.8% (overall accuracy: 93.9%). There was no statistical difference in the number of correctly identified the region of interest for the external tests between the expert endoscopist and the established model using attention map analysis (P = 0.11). In terms of the dysplasia subgroup, the number of correctly identified regions of interest was higher in the deep-learning model than in the endoscopist group, although statistically insignificant (P = 0.48). Conclusions: We established a deep-learning model that accurately classifies esophageal cancers, precursor lesions, and non-neoplasms. This model confirmed the potential for generalizability through multicenter external tests and explainability through the attention map analysis

Clinical Factors Associated with Obstructive Coronary Artery Disease in Patients with Out-of-Hospital Cardiac Arrest: Data from the Korean Cardiac Arrest Research Consortium (KoCARC) Registry

Background: Although coronary artery disease (CAD) is a major cause of out-of-hospital cardiac arrest (OHCA), there has been no convinced data on the necessity of routine invasive coronary angiography (ICA) in OHCA. We investigated clinical factors associated with obstructive CAD in OHCA. Methods: Data from 516 OHCA patients (mean age 58 years, 83% men) who underwent ICA after resuscitation was obtained from a nation-wide OHCA registry. Obstructive CAD was defined as the lesions with diameter stenosis >= 50% on ICA. Independent clinical predictors for obstructive CAD were evaluated using multiple logistic regression analysis, and their prediction performance was compared using area under the receiver operating characteristic curve with 10,000 repeated random permutations. Results: Among study patients, 254 (49%) had obstructive CAD. Those with obstructive CAD were older (61 vs. 55 years, P < 0.001) and had higher prevalence of hypertension (54% vs. 36%, P < 0.001), diabetes mellitus (29% vs. 21%, P = 0.032), positive cardiac enzyme (84% vs. 74%, P = 0.010) and initial shockable rhythm (70% vs. 61%, P = 0.033). In multiple logistic regression analysis, old age (>= 60 years) (odds ratio [On 2.01; 95% confidence interval [CI], 1.36-3.00; P = 0.001), hypertension (OR, 1.74; 95% CI, 1.18-2.57; P = 0.005), positive cardiac enzyme (OR, 1.72; 95% CI, 1.09-2.70; P = 0.019), and initial shockable rhythm (OR, 1.71; 95% CI, 1.16-2.54; P = 0.007) were associated with obstructive CAD. Prediction ability for obstructive CAD increased proportionally when these 4 factors were sequentially combined (P < 0.001). Conclusion: In patients with OHCA, those with old age, hypertension, positive cardiac enzyme and initial shockable rhythm were associated with obstructive CAD. Early ICA should be considered in these patients.Y