Factors underlying treatment decision-making for localized prostate cancer in the U.S. and Canada: A scoping review using principal component analysis

Abstract

Introduction: The purpose of this research is to gather, collate, and identify key factors commonly studied in localized prostate cancer (LPC) treatment decision-making in Canada and the U.S.Methods: This scoping review uses five databases (Medline, EMBASE, CINAHL, AMED, and PsycInfo) to identify relevant articles using a list of inclusion and exclusion criteria applied by two reviewers. A list of topics describing the themes of the articles was extracted and key factors were identified using principal component analysis (PCA). A word cloud of titles and abstracts of the relevant articles was created to identify complementary results to the PCA.Results: This review identified 77 relevant articles describing 32 topics related to LPC treatment decision-making. The PCA grouped these 32 topics into five key factors commonly studied in LPC treatment decision-making: 1) treatment type; 2) socioeconomic/demographic characteristics; 3) personal reasons for treatment choice; 4) psychology of treatment decision experience; and 5) level of involvement in the decision-making process. The word cloud identified common phrases that were complementary to the factors identified through the PCA.Conclusions: This research identifies several possible factors impacting LPC treatment decision-making. Further research needs to be completed to determine the impact that these factors have in the LPC treatment decision-making experience.

Neurovascular hypoxia after mild traumatic brain injury in juvenile mice correlates with heart–brain dysfunctions in adulthood

“…Hemoglobin was an important contributor to the differentiation between groups; however, measures of cardiac dysfunction had a larger influence on the analysis than oxygen saturation. These results align with previous work where PCA has been used to discriminate between clinically stratified groups and identify treatment approach factors 35,36 . Thus, PCA can distinguish between groups and identify underlying variables that may be important in the clinical management of patients with TBI.…”

“…These results align with previous work where PCA has been used to discriminate between clinically stratified groups and identify treatment approach factors. 35,36 Thus, PCA can distinguish between groups and identify underlying variables that may be important in the clinical management of patients with TBI.…”

The development of lived experience-centered word clouds to support research uncertainty gathering in degenerative cervical myelopathy: results from an engagement process and protocol for their evaluation, via a nested randomized controlled trial

“…A search of MEDLINE for “Wordcloud” or “Word Cloud,” including title and abstract screening, returned 53 articles (12 August 2019). Articles describe word cloud use to depict survey, workshop/forum, or medical literature data as well as other interesting applications to assist keyword identification for literature searches [ 24 , 25 ] and to evaluate reference letters for residency programs [ 26 ]. There are currently only two references to their use for stimulating ideas and both were deemed effective; first in palliative care, where word clouds were generated by a palliative patient and their family, to support positive memories during the bereavement process [ 27 ], and secondly, as part of an education initiative asking medical students to consider “What is professionalism?” [ 28 ].…”

The Impact of Geographic Location on Saskatchewan Prostate Cancer Patient Treatment Choices: A Multilevel and Spatial Analysis

“…46 The potential limitations of this study include the lack of information on other treatment decision factors, including patient preferences, that possibly influence treatment choice. 47 Data on patient comorbidity were also not available. This study was not able to distinguish those who were on watchful waiting versus active surveillance due to the nature of the population-level data.…”