Development of a rehabilitation programme for young adult cancer survivors using co-production.

Background: Young adult cancer survivors, defined as individuals aged 18-39 who have completed primary curative treatment, face numerous age-specific biopsychosocial late effects that impact health-related quality of life negatively. Rehabilitation can enhance participation in life roles, work, leisure activities and health-related quality of life. However, there is a lack of age-specific cancer rehabilitation for this population, leaving many young adults with diminished self-efficacy in managing their challenges, resulting in unmet needs.

International Metabolic Prognostic Index Is Superior to Other Metabolic Tumor Volume-Based Prognostication Methods in a Real-Life Cohort of Diffuse Large B-Cell Lymphoma.

Baseline metabolic tumor volume (MTV) is a promising prognostic marker in diffuse large B-cell lymphoma (DLBCL). We assessed the prognostic value of 4 novel metabolic risk scores in a real-life DLBCL cohort and compared them with the revised International Prognostic Index (IPI). We included a consecutive series of untreated DLBCL, not otherwise specified cases that were diagnosed in our hospital from 2008 to 2021 with available baseline [F]FDG PET/CT.

Introducing patient stories in health sciences education: the learning experiences of students.

Background: It has been advocated that the development of medical school curricula must be informed by students, doctors in training, educators, employers, other health and social care professionals and patients, families and carers. Patients are widely employed to teach clinical and interpersonal skills, and while recognised as crucial in health education, they have mostly been offered a passive role. We assessed the impact of patients contributing personal illness narratives in the master curriculum of allied health care professionals on students' learning experiences.

lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses.

Background: The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and accuracy remains a significant challenge due to variations in data recording and reporting standards.

Methods: We developed lab2clean, a novel algorithm aimed at automating and standardizing the cleaning of retrospective clinical laboratory results data.