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Bone & Joint Open
Vol. 5, Issue 1 | Pages 9 - 19
16 Jan 2024
Dijkstra H van de Kuit A de Groot TM Canta O Groot OQ Oosterhoff JH Doornberg JN

Aims

Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool.

Methods

A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.


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.


The Bone & Joint Journal
Vol. 100-B, Issue 12 | Pages 1535 - 1541
1 Dec 2018
Farrow L Ablett AD Mills L Barker S

Aims

We set out to determine if there is a difference in perioperative outcomes between early and delayed surgery in paediatric supracondylar humeral fractures in the absence of vascular compromise through a systematic review and meta-analysis.

Materials and Methods

A literature search was performed, with search outputs screened for studies meeting the inclusion criteria. The groups of early surgery (ES) and delayed surgery (DS) were classified by study authors. The primary outcome measure was open reduction requirement. Meta-analysis was performed in the presence of sufficient study homogeneity. Individual study risk of bias was assessed using the Risk of Bias in Non-Randomised Studies – of Interventions (ROBINS-I) criteria, with the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) criteria used to evaluate outcomes independently.


Bone & Joint Research
Vol. 7, Issue 5 | Pages 336 - 342
1 May 2018
Hotham WE Malviya A

This systematic review examines the current literature regarding surgical techniques for restoring articular cartilage in the hip, from the older microfracture techniques involving perforation to the subchondral bone, to adaptations of this technique using nanofractures and scaffolds. This review discusses the autologous and allograft transfer systems and the autologous matrix-induced chondrogenesis (AMIC) technique, as well as a summary of the previously discussed techniques, which could become common practice for restoring articular cartilage, thus reducing the need for total hip arthroplasty. Using the British Medical Journal Grading of Recommendations, Assessment, Development and Evaluation (BMJ GRADE) system and Grade system. Comparison of the studies discussed shows that microfracture has the greatest quantity and quality of research, whereas the newer AMIC technique requires more research, but shows promise.

Cite this article: W. E. Hotham, A. Malviya. A systematic review of surgical methods to restore articular cartilage in the hip. Bone Joint Res 2018;7:336–342. DOI: 10.1302/2046-3758.75.BJR-2017-0331.