Journal of surgical oncology
October 16, 2026 · via Europe PMC
Patient-Reported Outcome Measures in Musculoskeletal Oncology: Current Concepts, Barriers, and the Future.
Khan ST, Barker-Clarke RJ, Benyamini B, Burke ZD, Mesko NW, Nystrom LM
AI summary — for doctors, generated from the abstract only
PROMs are underused in musculoskeletal oncology, and this review outlines instruments, barriers, and future directions.
What was studied
A narrative review of PROM instruments such as TESS, PROMIS, and the Sarcoma Assessment Measure.
Key findings
- Only 4.9% of tumor-focused articles incorporated PROMs
- Barriers include psychological distress, workflow complexity, and disease heterogeneity
- Future directions include EHR dashboards, registry linkage, wearables, and AI
Clinical relevance
Better PROM use could support patient-centered care in sarcoma.
Who should care
Limitations
Short abstract, no systematic methods or results described.
PROM adoption needs workflow and technology solutions.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Patient-reported outcome measures (PROMs) remain underutilized in musculoskeletal oncology, with only 4.9% of tumor-focused articles incorporating them. This review synthesizes current PROM instruments-including Toronto Extremity Salvage Score, PROMIS, and emerging tools such as the Sarcoma Assessment Measure-and identifies barriers unique to this population, including psychological distress, workflow complexity, and disease heterogeneity. Future directions include flexible baseline collection, EHR-integrated dashboards, national registry linkage, wearable technology, and AI-driven processing to increase PROMs utilization.
DOI
10.1002/jso.70424
PMID
42885495
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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