Importance Existing prognostic cancer tools include biological and laboratory variables.
However, patients often do not know this information, preventing them from using the tools …

Importance To optimize palliative care in patients with cancer who are in their last year of
life, timely and accurate prognostication is needed. However, available instruments for …

Background: Predictive cancer tools focus on survival; none predict severe symptoms. Aim:
To develop and validate a model that predicts the risk for having low performance status and …

Prognostication of life expectancy is of utmost importance to patients, families, and
oncologists, particularly in the advanced cancer setting. Accurate prognostication is …

Importance Survivorship involves a multidisciplinary approach to surveillance and
management of comorbidities and secondary cancers, overseen by oncologists, surgeons …

It is important to identify prognostic and predictive factors concerning both life expectancy
and quality of life in palliative care patients to facilitate ethical, clinical, and organizational …

Background Prognosis is a key driver of clinical decision-making. However, available
prognostication tools have limited accuracy and variable levels of validation. Methods …

Importance To date, oncologist and model prognostic performance have been assessed
independently and mostly retrospectively; however, how model prognostic performance …

Background Describe and evaluate the methodological conduct of prognostic prediction
models developed using machine learning methods in oncology. Methods We conducted a …

Background Prognostic models are used widely in the oncology domain to guide medical
decision-making. Little is known about the risk of bias of prognostic models developed using …