ArticleThe Journal of arthroplasty2023
Demystifying Statistics and Machine Learning in Analysis of Structured Tabular Data.
Article in The Journal of arthroplasty, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Machine Learning in the Diagnosis and Prognosis of Mild Traumatic Brain Injury Using Diffusion Tensor Imaging: A Systematic Review.Journal of magnetic resonance imaging : JMRI · 2026Pooled it
- New approach methodologies (NAMs) for preclinical and translational evaluation of mRNA-lipid nanoparticle (LNP) therapeutics.Journal of controlled release : official journal of the Controlled Release Society · 2026Review
- Machine-learning prediction of urine-culture positivity in a multicentre test-ordered cohort: Model development and internal validation.BJUI compass · 2026Article
- Development and validation of a machine learning model for evidence of prior or current HCV infection: a demographic screening approach for the US population.BMC public health · 2026Article
- Machine Learning for Revision Joint Arthroplasty: A Systematic Review of Distinct Challenges, Current Performance, and Clinical Readiness.Arthroplasty today · 2026Article
- Predictors of self-reported sexually transmitted infections (STIs) among men in 54 low and middle income countries (LMICs): a comparison of deep learning and classical machine learning algorithms.BMC infectious diseases · 2026Article
- Sleep duration, sleep quality, and daytime napping in relation to incident cardiometabolic multimorbidity across three national aging cohorts: a prospective, interpretable machine learning study.Frontiers in nutrition · 2026Article
- Predicting Treatment Response to Pulsed Radiofrequency in Postherpetic Neuralgia Using an Explainable Machine Learning Model.Journal of pain research · 2026Article
- A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology.Renal failure · 2025Article
- Multifunctional Nanomaterial-Integrated Hydrogels for Sustained Drug Delivery: From Synthesis and Characterization to Biomedical Application.Gels (Basel, Switzerland) · 2025Review
- A development of machine learning models to preoperatively predict insufficient clinical improvement after total knee arthroplasty.Journal of orthopaedic surgery and research · 2025Article
- Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia: further analysis of the 2011 and 2016 Ethiopian Demographic and Health Survey.Frontiers in public health · 2025Article
- Leveraging multivariate analysis and adjusted mutual information to improve stroke prediction and interpretability.Neurosciences (Riyadh, Saudi Arabia) · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Electronic health records have facilitated the extraction and analysis of a vast amount of data with many variables for clinical care and research. Conventional regression-based statistical methods may not capture all the complexities in high-dimensional data analysis. Therefore, researchers are increasingly using machine learning (ML)-based methods to better handle these more challenging datasets for the discovery of hidden patterns in patients' data and for classification and predictive purposes. This article describes commonly used ML methods in structured data analysis with examples in orthopedic surgery. We present practical considerations in starting an ML project and appraising published studies in this field.
Indexed as
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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.