ArticleJCO oncology practice2021
Identification of Patient-Reported Outcome Phenotypes Among Oncology Patients With Palliative Care Needs.
Article in JCO oncology practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Using Patient-Reported Outcome Measures to Identify Mobility Limitation in Incurable Cancer.Archives of rehabilitation research and clinical translation · 2026Article
- Developing the multidisciplinary and multispecialty workforce and the systems needed to sustain high-quality care from diagnosis through long-term survivorship.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Review
- Article
- Symptom clusters associated with complementary and alternative medicine use by cancer patients: A cross-sectional study.PloS one · 2023Article
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 1 country.
Funding
Abstract
purposeDespite evidence-based guidelines recommending early palliative care, it remains unclear how to identify and refer oncology patients, particularly in settings with constrained access to palliative care. We hypothesize that patient-reported outcome (PRO) data can be used to characterize patients with palliative care needs. To determine if PRO data can identify latent phenotypes that characterize indications for specialty palliative care referral.
methodsWe conducted a retrospective study of self-reported symptoms on the Edmonton Symptom Assessment System collected from solid tumor oncology patients (n = 745) referred to outpatient palliative care. Data were collected as part of routine clinical care from October 2012 to March 2018 at eight community and academic sites. We applied latent profile analysis to identify PRO phenotypes and examined the association of phenotypes with clinical and demographic characteristics using multinomial logistic regression.
resultsWe identified four PRO phenotypes: (1) Low Symptoms (n = 295, 39.6%), (2) Moderate Pain/Fatigue + Mood (n = 180, 24.2%), (3) Moderate Pain/Fatigue + Appetite + Dyspnea (n = 201, 27.0%), and (4) High Symptoms (n = 69, 9.3%). In a secondary analysis of 421 patients, we found that two brief items assessing social and existential needs aligned with higher severity symptom and psychological distress phenotypes.
conclusionOncology patients referred to outpatient palliative care in a real-world setting can be differentiated into clinically meaningful phenotypes using brief, routinely collected PRO measures. Latent modeling provides a mechanism to use patient-reported data on a population level to identify distinct subgroups of patients with unmet palliative needs.
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