10 practical priorities to prevent and manage serious allergic reactions: GALEN ANACare and EFA Anaphylaxis Manifesto.

This Anaphylaxis Manifesto calls on communities to prioritise 10 practical actions to improve the lives of people at risk of serious allergic reactions. The Global Allergy and Asthma European Network and the European Federation of Allergy and Airways Diseases Patients' Associations (EFA) compiled patient-centric priorities. We used qualitative consensus methods, research evidence and feedback from over 200 patient groups, stakeholder organisations and healthcare professionals.

Analysis of Population Differences in Digital Conversations About Cancer Clinical Trials: Advanced Data Mining and Extraction Study.

Background: Racial and ethnic diversity in clinical trials for cancer treatment is essential for the development of treatments that are effective for all patients and for identifying potential differences in toxicity between different demographics. Mining of social media discussions about clinical trials has been used previously to identify patient barriers to enrollment in clinical trials; however, a comprehensive breakdown of sentiments and barriers by various racial and ethnic groups is lacking.

Objective: The aim of this study is to use an innovative methodology to analyze web-based conversations about cancer clinical trials and to identify and compare conversation topics, barriers, and sentiments between different racial and ethnic populations.

Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review.

Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment.

Standing Up Together.

Nurses represent the heart, soul, and future of the movement to end cancer. We are the ultimate collaborators. We interact across the spectrum of healthcare professionals, and we know the patients better than anyone.

Development and validation of a clinical prediction rule for hospitalization due to pneumonia or influenza or death during influenza epidemics among community-dwelling elderly persons.

Background: Uncertainties among health care providers and patients about the risk of serious influenza-associated complications and the potential benefits of vaccination may contribute to unsatisfactorily low influenza vaccination rates. To quantify the risk of serious outcomes (hospitalization due to pneumonia or influenza or death due to any cause) during influenza seasons, we developed a clinical prediction rule for the probability of hospitalization due to pneumonia or influenza or death among elderly persons.

Methods: We developed the clinical prediction rule using data from linked administrative databases in a cohort of 16,280 noninstitutionalized and unvaccinated elderly persons.

Influence of high-risk medical conditions on the effectiveness of influenza vaccination among elderly members of 3 large managed-care organizations.

This serial cohort study assessed the risk of hospitalization or death associated with influenza and the effectiveness of influenza vaccination among subgroups of elderly members of 3 managed-care organizations in the United States. Data on baseline characteristics and outcomes were obtained from computerized databases. A total of 122,974 (1996-1997 season) and 158,454 (1997-1998 season) persons were included in the cohorts.