Eric Pellegrino, Coralie Jacques, Nathalie Beaufils, Isabelle Nanni, Antoine Carlioz, Philippe Metellus, L'Houcine Ouafik
Machine learning random forest for predicting oncosomatic variant NGS analysis
Scientific reports
determine medical students views various aspects career radiation oncology ro identify areas may benefit reform guide initiatives stimulate broader diverse student interest specialty electronic survey sent student oncology interest group members seven us medical schools survey asked students rate 19 aspects ro 5point bipolar likerttype scale descriptive statistics reported along subgroup analyses based participants demographics response rate 511 n 275 538 favorably rated aspects ro outpatient working hours mean sd likerttype rating 451 082 routinely working physicians 445 076 use advanced technology treat patients unfavorably rated aspects ro less geographic flexibility residency employment 198 104 spending lot time computer treatment planning 280 121 job well understood doctors general public 289 102 gender associated significant differences 8 19 questions aspect ro viewed differences observed based race ethnicity though asian participants significantly favorable view ro scienceoriented specialty compared white underrepresented students respectively 350 versus 321 versus 284 p 01 findings inform ro community development effective initiatives encourage students fully explore specialty