ArticleScientific reports2021
Neighbors-based prediction of physical function after total knee arthroplasty.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- The global profile of individuals undergoing total knee replacement surgery through a PROGRESS-PLUS equity lens: A systematic review.The South African journal of physiotherapy · 2026Review
- Patient and Clinician Perceptions of a "People-Like-Me" Tool for Personalized Rehabilitation After Total Knee Arthroplasty: A Qualitative Interview Study.Physical therapy · 2025Article
- Implementation of a "People-Like-Me" Tool for Personalized Rehabilitation After Total Knee Arthroplasty: A Mixed Methods Pilot Study.Journal of evaluation in clinical practice · 2025Article
- Personalized Reference Values for the Two-Minute Walk Test: An Analysis of Cross-Sectional Data From the National Institutes of Health Toolbox Study.Archives of physical medicine and rehabilitation · 2023Observational
- Comparing "people-like-me" and linear mixed model predictions of functional recovery following knee arthroplasty.Journal of the American Medical Informatics Association : JAMIA · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The purpose of this study was to develop and test personalized predictions for functional recovery after Total Knee Arthroplasty (TKA) surgery, using a novel neighbors-based prediction approach. We used data from 397 patients with TKA to develop the prediction methodology and then tested the predictions in a temporally distinct sample of 202 patients. The Timed Up and Go (TUG) Test was used to assess physical function. Neighbors-based predictions were generated by estimating an index patient's prognosis from the observed recovery data of previous similar patients (a.k.a., the index patient's "matches"). Matches were determined by an adaptation of predictive mean matching. Matching characteristics included preoperative TUG time, age, sex and Body Mass Index. The optimal number of matches was determined to be m = 35, based on low bias (- 0.005 standard deviations), accurate coverage (50% of the realized observations within the 50% prediction interval), and acceptable precision (the average width of the 50% prediction interval was 2.33 s). Predictions were well-calibrated in out-of-sample testing. These predictions have the potential to inform care decisions both prior to and following TKA surgery.
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Registered trials
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