ArticlePloS one2021
Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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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
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Artificial intelligence in osteoporosis screening and fracture risk prediction among aging populations: a bibliometric evidence map.Frontiers in endocrinology · 2026Pooled it
- Weighing the impact of evidence in orthopaedic trauma registries: a systematic review of national and international registry data.BMJ open · 2025Pooled it
- Surgical timing and outcomes after volar plate fixation of distal radius fractures: A matched cohort study.PloS one · 2026Article
- AI-driven Technologies for Wrist Fracture Prediction: A Narrative Review of Emerging Approaches.Journal of wrist surgery · 2025Article
- Leveraging data to transform surgical outcomes and precision in orthopaedics.Journal of clinical orthopaedics and trauma · 2025Review
- A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making.BMC medical informatics and decision making · 2025Review
- Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images.BMC musculoskeletal disorders · 2025Article
- Can Machine Learning Identify Patients Who are Appropriate for Outpatient Open Reduction and Internal Fixation of Distal Radius Fractures?Journal of hand surgery global online · 2024Article
- Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review.Medical devices (Auckland, N.Z.) · 2024Article
- Comparing Artificial intelligence to physicians' competences in the domain of clinical reasoning: A systematic review and meta-analysis.Journal of medical education and curricular developmentReview
Corrections and comments
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Authors and funding
14 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDistal radius (wrist) fractures are the second most common fracture admitted to hospital. The anatomical pattern of these types of injuries is diverse, with variation in clinical management, guidelines for management remain inconclusive, and the uptake of findings from clinical trials into routine practice limited. Robust predictive modelling, which considers both the characteristics of the fracture and patient, provides the best opportunity to reduce variation in care and improve patient outcomes. This type of data is housed in unstructured data sources with no particular format or schema. The "Predicting fracture outcomes from clinical Registry data using Artificial Intelligence (AI) Supplemented models for Evidence-informed treatment (PRAISE)" study aims to use AI methods on unstructured data to describe the fracture characteristics and test if using this information improves identification of key fracture characteristics and prediction of patient-reported outcome measures and clinical outcomes following wrist fractures compared to prediction models based on standard registry data. METHODS AND
designAdult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study. The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons on EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS. DISCUSSION: The PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture.
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Registered trials
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.