Temporal validation of a Generalized Additive Model (GAM) to assess the risk of Chronic Kidney Disease (CKD).

Objective: To assess the temporal validity of a model predicting the risk of Chronic Kidney Disease (CKD) using Generalized Additive Models (GAM).

Materials: We adopted the Italian Health Search Database (HSD) with which the original algorithm was developed and validated by comparing different machine learnings models.

Methods: We selected all patients aged >=15 being active in HSD in 2019.

To predict the risk of chronic kidney disease (CKD) using Generalized Additive2 Models (GA2M).

Objective: To train and test a model predicting chronic kidney disease (CKD) using the Generalized Additive2 Model (GA2M), and compare it with other models being obtained with traditional or machine learning approaches.

Materials: We adopted the Health Search Database (HSD) which is a representative longitudinal database containing electronic healthcare records of approximately 2 million adults.

Methods: We selected all patients aged 15 years or older being active in HSD between January 1, 2018 and December 31, 2020 with no prior diagnosis of CKD.

Quantitative Analysis of Performance Recovery in Semi-Professional Football Players after the COVID-19 Forced Rest Period.

This study proposes the instrumental analysis of the physiological and biomechanical adaptation of football players to a fatigue protocol during the month immediately after the COVID-19 lockdown, to get insights into fitness recovery. Eight male semi-professional football players took part in the study and filled a questionnaire about their activity during the lockdown. At the resumption of activities, the mean heart rate and covered distances during fatiguing exercises, the normalized variations of mean and maximum exerted power in the Wingate test and the Bosco test outcomes (i.