Descriptive statistics of the study population, grouped by age. by Anmol Arora (9674334)

Comparison of general characteristics of lost to follow-up and non-lost to follow-up patients and simple age and sex-adjusted logistic regression. by Larissa Djembi Fossi (12174611)

Comparison of general characteristics of lost to follow-up and non-lost to follow-up patients and simple age and sex-adjusted logistic regression. by Larissa Djembi Fossi (12174611)

Variable descriptions. by Li Junfeng (13827388)

Descriptive statistics. by Li Junfeng (13827388)

Description of variables. by Jing He (58446)

Description of clusters in Differential Altas. by Litian Han (17350823)

Descriptive statistics by genders. by Josleen Al Barathie (12274065)

Descriptive statistics. by Mrinal Saha (11580507)

Description of dependent variable or feature. by Mst. Rokeya Khatun (22226685)

“…This study investigates the factors influencing school dropout among students aged 6–24 years, employing data from the 2019 Multiple Indicator Cluster Survey (MICS). …”

Optimization Algorithm Applied to Environmental Control in Broiler Houses by MAC Benvenga (10447461)

“…The process suggested that the optimized functions lead to absolute values obtained by the algorithm for each of the environmental factors of the controlled environmental system, representing the optimal condition of the environment found for each broiler age, considering the interactions of the variables. …”

The CeTI-Age-Kinematics dataset: A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults by Loreen Pogrzeba (19645765)

“…<p dir="ltr">The <i>CeTI-Age-Kinematics</i> dataset provides <b>full-body movements from 30 common daily tasks in nine categories</b>, e.g., targeted reaching, lifting and walking tasks, and of multiple object interactions. …”

Violin plot for numerical feature age. by Mst. Rokeya Khatun (22226685)

“…This study investigates the factors influencing school dropout among students aged 6–24 years, employing data from the 2019 Multiple Indicator Cluster Survey (MICS). …”

Overall model performance. by Theogene Kubahoniyesu (20391184)

“…The ML models constantly highlighted that woman with advanced maternal age, those in multiple unions, and inadequate ANC were more susceptible to adverse pregnancy outcomes.…”

Overall model performance. by Theogene Kubahoniyesu (20391184)

“…The ML models constantly highlighted that woman with advanced maternal age, those in multiple unions, and inadequate ANC were more susceptible to adverse pregnancy outcomes.…”

Identifying cognitive impairment with story recall (Wilson et al., 2024) by Sarah C. Wilson (19370966)

“…Discourse data were used to classify participants testing within range of cognitive impairment using multiple machine learning algorithms and stability analysis for identifying reliably predictive features in an effort to maximize prediction accuracy. …”

CMU SC and BOLD fMRI by Arian Ashourvan (6563300)

“…QA is an orientation distribution function (ODF) based index that is scaled with spin density information that permits the removal of isotropic diffusion components from the ODF to filter false peaks, facilitating the resolution of fiber tracts using deterministic fiber tracking algorithms. For a detailed description and comparison of QA with standard FA techniques, see (Yeh et al 2013). …”

Table 1_Pancreatitis associated with immune checkpoint inhibitors: a pharmacovigilance analysis based on FDA adverse event reporting system (FAERS) database.docx by Lingli Huang (309787)

“…</p>Methods<p>Data were extracted from the FAERS database from the first quarter of 2011 to the third quarter of 2024. Descriptive analysis was used to represent the clinical features, while reporting odds ratio (ROR), proportional reported ratio (PRR), the Bayesian confidence propagation neural network (BCPNN) and the multiple Gamma Poisson Shrinker (MGPS) were used for disproportionation analysis. …”

Image 2_Pancreatitis associated with immune checkpoint inhibitors: a pharmacovigilance analysis based on FDA adverse event reporting system (FAERS) database.tif by Lingli Huang (309787)

“…</p>Methods<p>Data were extracted from the FAERS database from the first quarter of 2011 to the third quarter of 2024. Descriptive analysis was used to represent the clinical features, while reporting odds ratio (ROR), proportional reported ratio (PRR), the Bayesian confidence propagation neural network (BCPNN) and the multiple Gamma Poisson Shrinker (MGPS) were used for disproportionation analysis. …”

Image 1_Pancreatitis associated with immune checkpoint inhibitors: a pharmacovigilance analysis based on FDA adverse event reporting system (FAERS) database.tif by Lingli Huang (309787)

“…</p>Methods<p>Data were extracted from the FAERS database from the first quarter of 2011 to the third quarter of 2024. Descriptive analysis was used to represent the clinical features, while reporting odds ratio (ROR), proportional reported ratio (PRR), the Bayesian confidence propagation neural network (BCPNN) and the multiple Gamma Poisson Shrinker (MGPS) were used for disproportionation analysis. …”