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  1. 37301
    “…The biostatistics were visualized using a random-effects forest plot. We compared data from all articles with sufficient data on patients with complications (ie, cases) and patients without complications (ie, noncases) among the two groups, malnourished and normal nutrition, from albumin, transferrin, and TLC data. …”
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    Preoperative Albumin, Transferrin, and Total Lymphocyte Count as Risk Markers for Postoperative Complications After Total Joint Arthroplasty: A Systematic Review
  2. 37302
    “…As clinical data is being reported from around the globe, it becomes important to focus on local subjects in a global milieu, lest one misses the trees for the forest. Our study is a short retrospective analysis of the demographic and clinical profiles of subjects presenting with a mild flu-like illness to our hospital who were tested for COVID-19. …”
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    Profile of Patients Suspected to be COVID-19: A Retrospective Analysis of Early Pandemic Data
  3. 37303
    “…We used boosted decision tree regression, decision forest regression, Bayesian linear regression, and linear regression to predict personalized blood uric acid based on basic health checkup test results, dietary information, and sociodemographic characteristics. …”
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    Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison
  4. 37304
    “…STROBE was used for reporting quality assessment. We examined forest plots and conducted both fix-effects and random-effects to estimate prevalence by R version 3.6.2/R studio 1.2.1335 statistical software packages META version 4.9–9. …”
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    The prevalence of asymptomatic neurosyphilis among HIV-negative serofast patients in China: A meta-analysis
  5. 37305
    por Itakura, Hikaru, Wakiya, Ryoshiro
    Publicado 2020
    “…Their density irrespective of size of eels was most strongly determined by distance from the river mouth, followed by riverbank type according to random forest models. Eel density decreased with increasing distance from the freshwater tidal limit located about 100–150 m from the river mouth. …”
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    Habitat preference, movements and growth of giant mottled eels, Anguilla marmorata, in a small subtropical Amami-Oshima Island river
  6. 37306
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    An implementation history of primary health care transformation: Alberta’s primary care networks and the people, time and culture of change
  7. 37307
    “…DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used the data on documentation status from the Los Angeles Family and Neighborhood Survey (LAFANS) to develop a random forest classifier machine learning model. K-fold cross-validation was used to test model performance. …”
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    Comparison of Use of Health Care Services and Spending for Unauthorized Immigrants vs Authorized Immigrants or US Citizens Using a Machine Learning Model
  8. 37308
    “…METHODS: Urine samples from patients with suspected UTI were quantitatively plated onto blood agar and MacConkey agar plates in the microbiology lab of Wake Forest Baptist Medical Center. After overnight incubation, colonies were identified to the species level by MALDI-TOF system. …”
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    1267. Comparative activity of omadacycline against extended-spectrum beta-lactamase positive and negative Escherichia coli and Klebsiella pneumoniae strains recovered from urine specimens
  9. 37309
    “…[Image: see text] Figure 2. Forest plots for adjusted hazard ratio of in-ICU mortality in critically ill COVID-19 patients required intensive care. …”
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    514. Clinical features and outcomes of 112 patients with SARS-CoV-2 infections requiring intensive care in a public healthcare system in South Florida
  10. 37310
    “…In the statistical analysis, the funnel plot, Egger's test, and Begg's test were used to assess publication bias. The I(2) statistic, forest plot, and Cochran's Q-test were used to deal with heterogeneity. …”
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    Low Economic Class Might Predispose Children under Five Years of Age to Stunting in Ethiopia: Updates of Systematic Review and Meta-Analysis
  11. 37311
    por Li, Yong, Qi, Dandan, Zhu, Baoli, Ye, Xin
    Publicado 2021
    “…The AUC, the evaluation parameter of the prediction model which was built by RandomForest, was 0.7. Furthermore, two subgroups were divided by consensus clustering analysis, in which stage, grade, and T differed. …”
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    Analysis of m6A RNA Methylation-Related Genes in Liver Hepatocellular Carcinoma and Their Correlation with Survival
  12. 37312
    “…Risk ratios from individual studies were displayed in forest plots and the pooled hazard ratios (HR) of death and corresponding confidence intervals (CI) were calculated according to random-effects models. …”
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    Prognostic Significance of CD4+ and CD8+ Tumor-Infiltrating Lymphocytes in Head and Neck Squamous Cell Carcinoma: A Meta-Analysis
  13. 37313
    “…The top 20 C-index genes and 17 immune-related lncRNAs were included in prognostic model construction, and random forest and the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms were employed to select features. …”
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    Prognostic Signature of Immune Genes and Immune-Related LncRNAs in Neuroblastoma: A Study Based on GEO and TARGET Datasets
  14. 37314
    “…A nationwide survey of fireflies in 2009 across Singapore documented 11 species, including “Luciola sp. 2”, which is particularly noteworthy because the specimens were collected from a freshwater swamp forest in the central catchment area of Singapore and did not fit the descriptions of any known Luciola species. …”
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    Molecular Systematics of the Firefly Genus Luciola (Coleoptera: Lampyridae: Luciolinae) with the Description of a New Species from Singapore
  15. 37315
    “…The expression of p14/ARF was associated with several clinical and pathological characteristics. A random forest-based machine-learning algorithm (Boruta) was implemented to identify which variables were associated with p14/ARF expression. …”
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    P14/ARF-Positive Malignant Pleural Mesothelioma: A Phenotype With Distinct Immune Microenvironment
  16. 37316
    “…First, we combined a random forest machine learning and a generalized additive mixed model to estimate daily high resolution (1 km × 1 km) visibility over the region using satellite-based aerosol optical depth (AOD) and airport visibility data. …”
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    Estimation of ambient PM(2.5) in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing
  17. 37317
    “…MAIN OUTCOMES AND MEASURES: Machine learning models (logistic regression [LR], random forest, and extreme gradient boosting) were trained for 2 binary outcomes: postpartum depression (PPD) and postpartum mental health service utilization. …”
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    Comparison of Methods to Reduce Bias From Clinical Prediction Models of Postpartum Depression
  18. 37318
    “…The AUROC values were 0.80, 0.81, 0.71, 0.70, 0.74, and 0.60 for random forest, support vector machine, sequential organ failure assessment (SOFA) score, logistic organ dysfunction score (LODS), simplified acute physiology II score (SAPS II) and SIC score, respectively, in validation set. …”
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    Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database
  19. 37319
    “…We also tested Machine Learning approaches for developing the risk models: Random Forest, Stochastic Gradient Boosting and Artificial Neural Network. …”
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    A risk calculator to predict adult attention-deficit/hyperactivity disorder: generation and external validation in three birth cohorts and one clinical sample
  20. 37320
    “…Multiple machine learning models (multivariable logistic regression, elastic net regression, support vector machine, random forest, and gradient boosting) were made to predict quality indicators. …”
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    Predictors of 30-Day Mortality Among Dutch Patients Undergoing Colorectal Cancer Surgery, 2011-2016
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