Ziying Yang

Abstract Objectives: Return to work (RTW) is a key indicator of recovery for young and middle-aged maintenance hemodialysis (MHD) patients. Timely identification of patients with a low likelihood of RTW is crucial for precision rehabilitation assessment and risk-stratified management. This study aimed to develop and validate a machine learning-based predictive model for identifying influencing factors of current RTW status and enabling risk stratification among young and middle-aged MHD patients.

1 INTRODUCTION Polar lows (PLs) are intense, mesoscale, maritime cyclones occurring at high latitudes and developing over open water, often near a sea-ice edge or snow-covered continents during marine cold-air outbreaks (Rasmussen & Turner, 2003; Kolstad, 2006; Rojo et al., 2015; Stoll et al., 2018). These systems usually have horizontal scales ranging from 200 to 500 km with a maximum of 1000 km, and last from three to 36 hours (Bresson et al., 2022; Stoll et al., 2018).

CONFLICT OF INTEREST STATEMENT The authors declare that they have no conflict of interest. DATA AVAILABILITY STATEMENT All the data analysed here are openly available. CARRA reanalysis data on single and pressure levels from 1991 to present used in this study are publicly available from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (DOI: 10.24381/cds.713858f6 and 10.24381/cds.e3c841ad).