Evidence map›Paper›PMID 41568289›Full record

ReviewTherapeutic advances in musculoskeletal disease2026

Implementation of clinical decision support tools for treatment selection in knee osteoarthritis: a scoping review.

Jodie A Cochrane, Oliver Roberts, Karen Ribbons, Ross Clark, Yong Hao Pua, Tong Leng Tan, Lynn Thwin, Ying Ying Leung, Ming Han Lincoln Liow, Bryan Yijia Tan and 1 more

Abstract readReview
In one paragraph

Review in Therapeutic advances in musculoskeletal disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jodie A CochraneCentre for Rehab Innovations, University of Newcastle, Callaghan, NSW, Australia.ORCID https://orcid.org/0000-0001-8259-0478
Oliver RobertsRehabilitation Research Institute of Singapore, Nanyang Technological University, Lee Kong Chian School of Medicine, #14-03, 11 Mandalay Rd, Singapore 308232, Singapore.ORCID https://orcid.org/0009-0001-3677-9146
Karen RibbonsCentre for Rehab Innovations, University of Newcastle, Callaghan, NSW, Australia.
Ross ClarkSchool of Health and Sports Science, University of the Sunshine Coast, Sippy Downs, QLD, Australia.
Yong Hao PuaDepartment of Physiotherapy, Singapore General Hospital, Singapore, Singapore.
Tong Leng TanDepartment of Orthopaedic Surgery, Tan Tock Seng Hospital, Singapore, Singapore.
Lynn ThwinDepartment of Orthopaedic Surgery, Tan Tock Seng Hospital, Singapore, Singapore.
Ying Ying LeungDuke-NUS Medical School, Singapore, Singapore.ORCID https://orcid.org/0000-0001-8492-6342
Ming Han Lincoln LiowDuke-NUS Medical School, Singapore, Singapore.
Bryan Yijia TanRehabilitation Research Institute of Singapore (RRIS), Nanyang Technological University, Singapore, Singapore.
Michael NilssonCentre for Rehab Innovations, University of Newcastle, Callaghan, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Knee osteoarthritis (KOA) presents heterogeneous phenotypes, motivating a need for clinicians to deliver targeted therapies. There is a plethora of options that can be encompassed in KOA treatment regimes. Clinical decision support (CDS) tools that incorporate individual patient data have the capacity to tailor treatments to meet a patient's individual needs and assist with clinical decision-making. We aim to identify and evaluate CDS tools for individuals with KOA that use individualised prediction models to guide intervention decisions. A scoping review of the literature. A systematic search of six electronic databases, including Ovid Embase, Ovid Medline, Cochrane, CHINAL Ultimate, Scopus and Web of Science, was conducted for articles published between January 1, 2010 and May 17, 2024. Two reviewers independently screened articles and extracted data on study design, tool implementation and underlying prediction models. Eligible studies implemented personalised decision aids, designed to support clinical decisions regarding KOA interventions. The search yielded 5376 publications, of which 2445 were duplicates, leaving 2931 for screening. After title/abstract and full-text reviews, 14 studies were included in the final analysis, with one added through citation searching. Ten distinct decision aids were identified across the included studies. Most studies originated from the United States. Fewer than half of the decision aids included personalised information about non-surgical alternatives. Outcomes such as knee pain and physical function were the most commonly addressed, while psychosocial and financial impacts were rarely reported. Limited details were provided about the development and functionality of the underlying prediction models. Personalised decision aids for KOA show promise in supporting patient-centred decision-making. However, their clinical utility is constrained by limited transparency in model development and implementation. Future studies should emphasise the inclusion of non-surgical treatment options, early-stage KOA patients and personalised outcomes beyond pain and function to enhance their relevance and impact in clinical practice.

Indexed as

clinical decision support toolsknee osteoarthritismachine learningscoping reviewtreatment outcome

Identifiers

PMID41568289
PMCPMC12816537

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.