SynthesisJournal of orthopaedic research : official publication of the Orthopaedic Research Society2022
Machine learning prediction models in orthopedic surgery: A systematic review in transparent reporting.
Synthesis in Journal of orthopaedic research : official publication of the Orthopaedic Research Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 5 of them syntheses that pooled 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.
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
41 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation.Ewha medical journal · 2025Guideline
- Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis.European journal of haematology · 2023Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Machine learning prediction models in orthopedic surgery: A systematic review in transparent reporting.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2022Pooled it
- Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review.Acta orthopaedica · 2021Pooled it
- Early Radiographic Changes Predict Radiographic Stability in Distal Radius Fractures: Development and Validation of an Explainable Predictive Model.Journal of hand surgery global online · 2027Article
- Evaluation of short- and long-term mortality prediction in patients undergoing hip fracture surgery using a biomarker-based interpretable predictive modeling approach: a retrospective cohort analysis.BMC medical informatics and decision making · 2026Article
- Methodological Reporting Quality of Artificial Intelligence-Based Orthopedic Studies: A Literature Analysis.Medeniyet medical journal · 2026Article
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial intelligence and machine learning: an important new set of tools for clinical shoulder arthroplasty research.JSES international · 2026Review
- Prediction of Surgical Intervention in Acute Knee Trauma: A Focus on Threshold-Specific Performance and Clinical Decision Utility.Diagnostics (Basel, Switzerland) · 2026Article
- Development of machine learning models for preoperative prediction of insufficient clinical improvement after anterior cruciate ligament reconstruction.BMC musculoskeletal disorders · 2026Article
- AI dialogues in cartilage repair: which guides evidence-based decisions better?Frontiers in cell and developmental biology · 2026Article
- Quality assurance in machine Learning: Do we hit the mark for total joint arthroplasty models? A systematic review.Journal of clinical orthopaedics and trauma · 2025Review
- Secondary Prevention of AFAIS: Deploying Traditional Regression, Machine Learning, and Deep Learning Models to Validate and Update CHA2DS2-VASc for 90-Day Recurrence.Journal of clinical medicine · 2025Article
- Article
- The Emergence of Applied Artificial Intelligence in the Realm of Value Based Musculoskeletal Care.Current reviews in musculoskeletal medicine · 2025Review
- Temporal validation of the SORG 90-Day and 1-Year machine learning algorithms for survival of patients with spinal metastatic disease.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies.Journal of bone oncology · 2025Review
- Machine learning models for predicting tibial intramedullary nail length.BMC musculoskeletal disorders · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) studies are becoming increasingly popular in orthopedics but lack a critically appraisal of their adherence to peer-reviewed guidelines. The objective of this review was to (1) evaluate quality and transparent reporting of ML prediction models in orthopedic surgery based on the transparent reporting of multivariable prediction models for individual prognosis or diagnosis (TRIPOD), and (2) assess risk of bias with the Prediction model Risk Of Bias ASsessment Tool. A systematic review was performed to identify all ML prediction studies published in orthopedic surgery through June 18th, 2020. After screening 7138 studies, 59 studies met the study criteria and were included. Two reviewers independently extracted data and discrepancies were resolved by discussion with at least two additional reviewers present. Across all studies, the overall median completeness for the TRIPOD checklist was 53% (interquartile range 47%-60%). The overall risk of bias was low in 44% (n = 26), high in 41% (n = 24), and unclear in 15% (n = 9). High overall risk of bias was driven by incomplete reporting of performance measures, inadequate handling of missing data, and use of small datasets with inadequate outcome numbers. Although the number of ML studies in orthopedic surgery is increasing rapidly, over 40% of the existing models are at high risk of bias. Furthermore, over half incompletely reported their methods and/or performance measures. Until these issues are adequately addressed to give patients and providers trust in ML models, a considerable gap remains between the development of ML prediction models and their implementation in orthopedic practice.
Indexed as
Identifiers
What OpenQuestion holds
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.