TRAUMATIC CERVICAL SPINAL CORD INJURY. IS URGENT INTERVENTION SUPERIOR TO DELAYED INTERVENTION? A META-ANALYSIS EVALUATION by I Ketut Martiana, Donny Permana, Lukas Widhiyanto

“…The data was presented in odd ratio (OR) and confidence interval (CI) and were further analyzed by forest plot. Results: From PubMed, there were 353 articles, Embase 2 articles, and Cochrane 594 articles, but only 3 articles which fulfilled the inclusion criteria. …”
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The Impact of Positive Fluid Balance on Sepsis Subtypes: A Causal Inference Study by Sharad Patel, Adam Green, Yanika Wolfe, Gregory Felock, Samantha Epstein, Nitin Puri

“…Binarized positive fluid balance with mortality was examined using DoWhy’s logistic regression, while continuous data were analyzed with random forest T-learner. ATE served as the primary metric. …”
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Statins and the incidence of post-stroke depression: a systematic review and meta-analysis by Chaohua Cui, Jue Li, Weicong Chen

“…Appraisal and Synthesis Methods: Forest plot to display pooled results; I2 test to evaluate heterogeneity.ResultsOf the 37 studies selected, four were eligible. …”
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A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning by Shilan Abdullah Hassan, Maha Sabah Saeed

“…A comparative study evaluated the performance of five well-known two-class classification algorithms: two-class boosted decision trees, two-class decision forests, two-class locally deep SVMs, two-class neural networks, and two-class logistic regression. …”
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Interpretable machine learning for predicting sepsis risk in emergency triage patients by Zheng Liu, Wenqi Shu, Teng Li, Xuan Zhang, Wei Chong

“…In Model 2, Gradient Boosting achieved the highest AUC of 0.83, followed by Extra Tree, Random Forest, and Support Vector Machine (all 0.82). The SHAP method provided more comprehensible explanations for the Gradient Boosting algorithm. …”
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Climate-driven distribution shifts of Iranian amphibians and identification of refugia and hotspots for effective conservation by Somaye Vaissi, Alireza Mohammadi

“…GISS-E2-1-G suggests expansive refugia encompassing the Hyrcanian forests, Alborz, Zagros, and Kopet Dag mountains, along with the southern coast. …”
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Recognition model for major depressive disorder in Arabic user-generated content by Esraa M. Rabie, Atef F. Hashem, Fahad Kamal Alsheref

“…Results In binary classifications, we used ML techniques such as “support vector machine (SVM), random forest (RF), logistic regression (LR), and Gaussian naive Bayes (GNB),” and used BERT transformers “ARABERT.” …”
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Correlation between Soil Organic Matter, Total Organic Matter and Water Content with Climate and Depths of Soil at Different Land use in Kelantan, Malaysia by A Azlan, ER Aweng, CO Ibrahim, A Noorhaidah

“…The correlation of soil organic matter (SOM) content, total organic carbon (TOC) content, water content and soils texture for industrial area at Pengkalan Chepa, township of Kota Bharu district, agricultural area at Banggu and forested area in UMK, Jeli were investigated. These data sets were also correlated to temporal event in Kelantan State. …”
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Towards personalized cardiometabolic risk prediction: A fusion of exposome and AI by Zeinab Shahbazi, Slawomir Nowaczyk

“…For risk prediction, the Random Forest classifier was employed, with performance compared to an integrative ML model using clinical and physical variables. …”
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Atmospheric Methane Condition over the South Sumatera Peatland during the COVID-19 Pandemic by Muhammad Rendana, Wan Mohd Razi Idris, Sahibin Abdul Rahim

“…Thus, the restrictions during lockdown, which reduced anthropogenic activities, such as land use conversion and biomass burning, and related events, such as peatland and forest fires, significantly influenced the level of atmospheric CH4 above the peatlands in Indonesia.…”
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Machine Learning Applications to Dust Storms: A Meta-Analysis by Reem K. Alshammari, Omer Alrwais, Mehmet Sabih Aksoy

“…In contrast, the most used models for dust storm prediction are SVM and random forests that predict the occurrence of dust storms from meteorological data. …”
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MetaRange.jl: A Dynamic and Metabolic Species Range Model for Plant Species by Jana Blechschmidt, Juliano Sarmento Cabral

“…Our results show that climate change reduces habitat suitability overall, but some regions like the Franconian Forest and the Alps see increased suitability and abundance, confirming their role as refugia. …”
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A decision support system based on classification algorithms for the diagnosis of periodontal disease by Abdulrahman Alshehri, Mohammed Dahman, Mousa Assiri, Abdulkarim Alshehri, Sharifah Alqahtani, Mohammed Shaiban, Bashyer Alqahtani, Sabah Althbyani, Hatem Alhefdi, Khalid Hakami, Abdulbari Ali, Abdullah Saeed

“…Aims: The purpose of this study was to develop and evaluate a decision support system (DSS) based on selected classification algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and logistic regression for the periodontal disease diagnosis. …”
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Machine Learning Prediction of Peripheral Mononuclear Cells Based on Interactomic Hub Genes in Periodontitis and Rheumatoid Arthritis by Sri Shivasankari Thilagar, Pradeep Kumar Rathinavelu, Pradeep Kumar Yadalam

“…Result: Decision tree, AdaBoost, and Random Forest had an area under the receiver operating characteristic curve (AUC) in the receiver operating characteristic curve of 0.967, 1.000, and 0.973, respectively. …”
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AI Machine Learning–Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis by Hocheol Lee, Myung-Bae Park, Young-Joo Won

“…Machine learning algorithms, including random forest, gradient boosting model, light gradient boosting model, extreme gradient boosting model, and k-nearest neighbors, were employed for analysis. …”
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Evolution of Cerebral Atrophy in a Patient with Super Refractory Status Epilepticus Treated with Barbiturate Coma by Christopher R. Newey, Pravin George, Premkumar Nattanmai, Christine Ahrens, Stephen Hantus, Aarti Sarwal

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Análisis productivo y económico de sistemas silvo-apícola pastoriles en el norte de la provincia de Buenos Aires by P. FERRERE, A. SIGNORELLI, S.M. CABRINI

“…Este estudio tiene como objetivo hacer una evaluación de una propuesta de diversificación productiva para productores de la región pampeana continental a través de la incorporación de la actividad forestal junto con la producción de forraje y de miel. …”
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Test of the RUSLE and Key Influencing Factors Using GIS and Probability Methods: A Case Study in Nanling National Nature Reserve, South China by Jun Wang, Qian He, Ping Zhou, Qinghua Gong

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Geographic patterns and ecological causes of phylogenetic structure in mosses along an elevational gradient in the central Himalaya by Hong Qian, Oriol Grau

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Small Satellite Mission Concepts for Space Weather Research and as Pathfinders for Operations by Amir Caspi, M. Barthelemy, C. D. Bussy‐Virat, I. J. Cohen, C. E. DeForest, D. R. Jackson, A. Vourlidas, T. Nieves‐Chinchilla

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