Background: The aim of this study was to validate and compare the performance of statistical (Utstein‐Based Return of Spontaneous Circulation and Shockable Rhythm–Witness–Age–pH) and machine learning–based (Prehospital Return of Spontaneous Circulation and Swedish Cardiac Arrest Risk Score) models in predicting the outcomes following out‐of‐hospital cardiac arrest and to assess the impact of the COVID‐19 pandemic on the models' performance. Methods and Results: This retrospective analysis included adult patients with out‐of‐hospital cardiac arrest treated at 3 academic hospitals between 2015 and 2023. The primary outcome was neurological outcomes at hospital discharge. Patients were divided into pre‐ (2015–2019) and post‐2020 (2020–2023) subgroups to examine the effect of the COVID‐19 pandemic on out‐of‐hospital cardiac arrest outcome prediction. The models' performance was evaluated using the area under the receiver operating characteristic curve and compared by the DeLong test. The analysis included 2161 patients, 1241 (57.4%) of whom were resuscitated after 2020. The cohort had a median age of 69.2 years, and 1399 patients (64.7%) were men. Overall, 69 patients (3.2%) had neurologically intact survival. The area under the receiver operating characteristic curves for predicting neurological outcomes were 0.85 (95% CI, 0.83–0.87) for the Utstein‐Based Return of Spontaneous Circulation score, 0.82 (95% CI, 0.81–0.84) for the Shockable Rhythm–Witness–Age–pH score, 0.79 (95% CI, 0.78–0.81) for the Prehospital Return of Spontaneous Circulation score, and 0.79 (95% CI, 0.77–0.81) for the Swedish Cardiac Arrest Risk Score model. The Utstein‐Based Return of Spontaneous Circulation score significantly outperformed both the Prehospital Return of Spontaneous Circulation score ( P <0.001) and the Swedish Cardiac Arrest Risk Score model ( P =0.007). Subgroup analysis indicated no significant difference in predictive performance for patients resuscitated before versus after 2020. Conclusions: In this external validation, both statistical and machine learning–based models demonstrated excellent and fair performance, respectively, in predicting neurological outcomes despite different model architectures. The predictive performance of all evaluated clinical scoring systems was not significantly influenced by the COVID‐19 pandemic.

Background: Gastric cancer (GC) is a leading cause of cancer-related mortality, particularly in East Asia. Despite treatment advances, the prognosis remains poor owing to late diagnosis and high metastatic potential. Phosphorylated AXL (pAXL), a receptor tyrosine kinase, promotes cancer progression, including epithelial–mesenchymal transition (EMT), tumor growth, and metastasis. In this study, we aimed to investigate the relationship between pAXL expression and prognosis in patients with GC, focusing on survival outcomes and other biomarkers such as fibronectin and phosphorylated AKT (pAkt). Methods: Immunohistochemistry was performed to assess the expression of pAXL, fibronectin, and pAkt in 188 GC specimens collected between 2000 and 2013. H-scores were calculated based on staining intensity and percentage. The association between pAXL expression and patient outcomes was assessed using Kaplan–Meier survival analysis and multivariate logistic regression. Results: Higher pAXL expression was significantly associated with improved survival, particularly in male patients. pAXL expression positively correlated with fibronectin and pAkt upregulation, suggesting its role in promoting tumor invasion and EMT. Multivariate analysis identified pAXL, fibronectin, and pAkt as significant prognostic indicators, whereas other factors such as age, tumor grade, and tumor size were not statistically significant. Conclusions: This study identified pAXL as a valuable prognostic marker in GC, with higher expression levels associated with better survival outcomes, particularly in male patients. pAXL enhanced the invasive potential of GC cells through fibronectin and pAkt regulation, making it a promising therapeutic target. Further research is needed to explore the potential of pAXL-targeted therapies and better understand their role in cancer progression and treatment response.

J Indian Prosthodont Soc, Official publication of Indian Prosthodontic Society

Pulmonary embolism is a difficult disease to diagnose in the emergency department. It may be fatal if the diagnosis is missed. Clinical practice guidelines and textbooks publish pretest diagnostic tools and algorithms that facilitate the diagnosis of pulmonary embolism. We developed and administered a questionnaire that determined that such tools can be difficult to remember to use. We also designed an Android application to facilitate diagnosis of pulmonary embolism and used a questionnaire to evaluate the application.

Background: Extracorporeal cardiopulmonary resuscitation (ECPR) has demonstrated survival benefit in selected patients with out‐of‐hospital cardiac arrest, yet optimal selection criteria remain uncertain. Machine‐learning–based individualized treatment effect (ITE) modeling may identify patients most likely to benefit by capturing heterogeneity of treatment response, potentially providing a more accurate strategy for ECPR candidate selection than current rule‐based criteria. Methods: We retrospectively analyzed adult, nontraumatic, emergency medical services–attended patients with out‐of‐hospital cardiac arrest from 4 tertiary centers in Taiwan between 2016 and 2024. After propensity score matching for shockable rhythm and witnessed arrest, a gradient‐boosted trees–based causal forest model was developed to estimate ITE and predict the survival benefit of ECPR to hospital discharge. Absolute observed treatment effect across different ITE thresholds was compared with trial‐based (ARREST [Advanced reperfusion strategies for patients with out‐of‐hospital cardiac arrest and refractory ventricular fibrillation] trial, PRAGUE OHCA [Effect of Intra‐arrest Transport, Extracorporeal Cardiopulmonary Resuscitation, and Immediate Invasive Assessment and Treatment on Functional Neurologic Outcome in Refractory Out‐of‐Hospital Cardiac Arrest] trial, and INCEPTION [Early Extracorporeal CPR for Refractory Out‐of‐Hospital Cardiac Arrest] trial) and hospital‐specific criteria. Results: Among 1953 matched patients, 977 received ECPR. Overall survival was similar between ECPR and non‐ECPR groups (11.1% versus 12.8%; standardized mean difference=0.054). In the top 10% of patients with the highest predicted benefit, survival to discharge was 50.0% with ECPR versus 20.0% without, yielding an absolute observed treatment effect of 30.0% (95% CI, 1.56–57.58; P =0.042). The ITE model identified subgroups with higher observed survival benefit compared with those selected by rule‐based criteria. Factors associated with higher ECPR treatment effect included lower serum pH and partial pressure of carbon dioxide level, higher lactate level, younger age, and bystander cardiopulmonary resuscitation. Conclusions: The ITE‐based model was associated with greater observed survival benefit compared with current rule‐based criteria and may demonstrate potential as a data‐driven framework for candidate selection for ECPR. Further validation in prospective settings is warranted.