Evidence map›Paper›PMID 40205648›Full record

ReviewJournal of orthopaedic research : official publication of the Orthopaedic Research Society2025

Pain Assessment in Osteoarthritis: Present Practices and Future Prospects Including the Use of Biomarkers and Wearable Technologies, and AI-Driven Personalized Medicine.

Shujaa T Khan, Nick Huffman, Xiaojuan Li, Anukriti Sharma, Carl S Winalski, Eric T Ricchetti, Kathleen Derwin, Suneel S Apte, Daniel Rotroff, Carl Y Saab and 1 more

Abstract readReview
In one paragraph

Review in Journal of orthopaedic research : official publication of the Orthopaedic Research Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Shujaa T KhanDepartment of Orthopedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA.
Nick HuffmanDepartment of Orthopedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA.
Xiaojuan LiDepartment of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.ORCID 0000-0002-0567-9935
Anukriti SharmaDepartment of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Carl S WinalskiProgram of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, Ohio, USA.
Eric T RicchettiDepartment of Orthopedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA.
Kathleen DerwinDepartment of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Suneel S ApteDepartment of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Daniel RotroffDepartment of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Carl Y SaabDepartment of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Nicolas S PiuzziDepartment of Orthopedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA.ORCID 0000-0003-3007-7538

Funding

The proteolytic landscape of osteoarthritic cartilageR21AR078498 · NIAMS · CLEVELAND CLINIC LERNER COM-CWRU · PI APTE, SUNEEL S · 2022 to 2023
$390k
NIAMS NIH HHS R21 AR078498
6 · The paper itself

Abstract

Osteoarthritis (OA) is a highly prevalent chronic joint disorder affecting ~600 million individuals worldwide and is characterized by complex pain mechanisms that significantly impair patient quality of life. Challenges exist in accurately assessing and measuring pain in OA due to variations in pain perception among individuals and the heterogeneous nature of the disease. Conventional pain assessment methods, such as patient-reported outcome measures and clinical evaluations, often fail to fully capture the heterogeneity of pain experiences among individuals with OA. This review will summarize and evaluate current methods of pain assessment in OA and highlight future directions for standardized pain assessment. We discuss the role of animal models in enhancing our understanding of OA pain pathophysiology and highlight the necessity of translational research to advance pain assessment strategies. Key challenges explored include identifying phenotypes of pain susceptibility, integrating biomarkers into clinical practice, and adopting personalized pain management approaches through the incorporation of multi-modal data and multilevel analysis. We underscore the imperative for continued innovation in pain assessment and management to improve outcomes for patients with OA.

Indexed as

OsteoarthritisPain MeasurementPrecision MedicineWearable Electronic DevicesAnimalsBiomarkersHumansBiomarkersAIassessmentbiomarkersclinicalosteoarthritispainreview

Identifiers

PMID40205648
PMCPMC12159590

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.