ArticleThe patient2023
A Foundation for Patient-Centered Core Impact Sets: Key Learnings from Past and Existing Approaches.
Article in The patient, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- A Cross-Sectional Study on the Learning Needs and Satisfaction Levels of Orthopedics and Trauma Surgery Patients at Discharge.Journal of evaluation in clinical practice · 2025Article
- Partnered Recruitment: Engaging Individuals With Lived Experience in the Recruitment of Co-Design Participants.Health expectations : an international journal of public participation in health care and health policy · 2024Article
- Evidence inventory: A patient-centered framework for Centers for Medicare & Medicaid Services to assess the clinical benefit of drugs to inform its maximum fair price negotiation.Journal of managed care & specialty pharmacy · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite growing commitment to patient centricity, challenges persist in consistently identifying the impacts of disease and/or treatment that patients report as most important to them, especially across myriad potential downstream uses. Patient-centered core impact sets (PC-CIS), disease-specific lists of impacts that patients report as most important, are proposed as a solution. But, PC-CIS is a new concept, currently in the pilot stage with patient advocacy groups. We conducted an environmental scan to explore PC-CIS conceptual overlap with past/existing efforts [e.g., core outcome sets (COS)] and to inform general feasibility for further development and operationalization. With guidance and advice from an expert advisory committee, we conducted a search of the literature and relevant websites. Identified resources were reviewed for alignment with the PC-CIS definition, and key insights were gleaned. We identified 51 existing resources and five key insights: (1) no existing efforts identified meet the definition of PC-CIS as we have specified it in terms of patient centricity, (2) existing COS-development efforts are a valuable source of foundational resources for PC-CIS, (3) existing health-outcome taxonomies can be augmented with patient-prioritized impacts to create a comprehensive impact taxonomy, (4) current approaches/methods can inadvertently exclude patient priorities from core lists/sets and will need to be modified to protect the patient voice, and (5) there is need for clarity and transparency on how patients were engaged in individual past/existing efforts. PC-CIS is conceptually unique from past/existing efforts in its explicit emphasis on patient leadership and being patient driven. However, PC-CIS development can leverage many resources from the past/existing related work.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.