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Bone & Joint Open
Vol. 4, Issue 5 | Pages 315 - 328
5 May 2023
De Klerk TC Dounavi DM Hamilton DF Clement ND Kaliarntas KT

Aims

The aim of this study was to determine the effectiveness of home-based prehabilitation on pre- and postoperative outcomes in participants awaiting total knee (TKA) and hip arthroplasty (THA).

Methods

A systematic review with meta-analysis of randomized controlled trials (RCTs) of prehabilitation interventions for TKA and THA. MEDLINE, CINAHL, ProQuest, PubMed, Cochrane Library, and Google Scholar databases were searched from inception to October 2022. Evidence was assessed by the PEDro scale and the Cochrane risk-of-bias (ROB2) tool.


The Bone & Joint Journal
Vol. 106-B, Issue 11 | Pages 1216 - 1222
1 Nov 2024
Castagno S Gompels B Strangmark E Robertson-Waters E Birch M van der Schaar M McCaskie AW

Aims

Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials.

Methods

A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures.


Aims

The aim of this study was to review the current evidence surrounding curve type and morphology on curve progression risk in adolescent idiopathic scoliosis (AIS).

Methods

A comprehensive search was conducted by two independent reviewers on PubMed, Embase, Medline, and Web of Science to obtain all published information on morphological predictors of AIS progression. Search items included ‘adolescent idiopathic scoliosis’, ‘progression’, and ‘imaging’. The inclusion and exclusion criteria were carefully defined. Risk of bias of studies was assessed with the Quality in Prognostic Studies tool, and level of evidence for each predictor was rated with the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. In all, 6,286 publications were identified with 3,598 being subjected to secondary scrutiny. Ultimately, 26 publications (25 datasets) were included in this review.