Evidence map›Paper›PMID 38457182›Full record

Trial reportJAMA network open2024

Predictive Tool Use and Willingness for Surgery in Patients With Knee Osteoarthritis: A Randomized Clinical Trial.

Yushy Zhou, Lauren Patten, Tim Spelman, Samantha Bunzli, Peter F M Choong, Michelle M Dowsey, Chris Schilling

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.7field-weighted citation impact, top 18% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
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

7 authors at 3 institutions in 1 country.

Yushy ZhouDepartment of Surgery, The University of Melbourne, Melbourne, Victoria, Australia.
Lauren PattenDepartment of Surgery, The University of Melbourne, Melbourne, Victoria, Australia.
Tim SpelmanDepartment of Surgery, The University of Melbourne, Melbourne, Victoria, Australia.
Samantha BunzliSchool of Health Sciences and Social Work, Griffith University, Nathan Campus, Brisbane, Queensland, Australia.
Peter F M ChoongDepartment of Surgery, The University of Melbourne, Melbourne, Victoria, Australia.
Michelle M DowseyDepartment of Surgery, The University of Melbourne, Melbourne, Victoria, Australia.
Chris SchillingDepartment of Surgery, The University of Melbourne, Melbourne, Victoria, Australia.
University of Melbourne · AUSt Vincent's Hospital · AURoyal Brisbane and Women's Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Despite the increasing number of tools available to predict the outcomes of total knee arthroplasty (TKA), the effect of these predictive tools on patient decision-making remains uncertain. Objective: To assess the effect of an online predictive tool on patient-reported willingness to undergo TKA. Design, Setting, and Participants: This parallel, double-masked, 2-arm randomized clinical trial compared predictive tool use with treatment as usual (TAU). The study was conducted between June 30, 2022, and July 31, 2023. Participants were followed up for 6 months after enrollment. Participants were recruited from a major Australian private health insurance company and from the surgical waiting list for publicly funded TKA at a tertiary hospital. Eligible participants had unilateral knee osteoarthritis, were contemplating TKA, and had previously tried nonsurgical interventions, such as lifestyle modifications, physiotherapy, and pain medications. Intervention: The intervention group was provided access to an online predictive tool at the beginning of the study. This tool offered information regarding the likelihood of improvement in quality of life if patients chose to undergo TKA. The predictions were based on the patient's age, sex, and baseline symptoms. Conversely, the control group received TAU without access to the predictive tool. Main Outcomes and Measures: The primary outcome measure was the reduction in participants' willingness to undergo surgery at 6 months after tool use as measured by binomial logistic regression. Secondary outcome measures included participant treatment preference and the quality of their decision-making process as measured by the Knee Decision Quality Instrument. Results: Of 211 randomized participants (mean [SD] age, 65.8 [8.3] years; 118 female [55.9%]), 105 were allocated to the predictive tool group and 106 to the TAU group. After adjusting for baseline differences in willingness for surgery, the predictive tool did not significantly reduce the primary outcome of willingness for surgery at 6 months (adjusted odds ratio, 0.85; 95% CI, 0.42-1.71; P = .64). Conclusions and Relevance: Despite the absence of treatment effect on willingness for TKA, predictive tools might still enhance health outcomes of patients with knee osteoarthritis. Additional research is needed to optimize the design and implementation of predictive tools, address limitations, and fully understand their effect on the decision-making process in TKA. Trial Registration: ANZCTR.org.au Identifier: ACTRN12622000072718.

Indexed as

Osteoarthritis, KneeAgedAustraliaFemaleHumansKnee JointMaleMiddle AgedQuality of Life

Identifiers

PMID38457182
PMCPMC10924247
OpenAlexW4392601531

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.