ArticleCirculation. Cardiovascular quality and outcomes2020
Recommendations for Reporting Machine Learning Analyses in Clinical Research.
Article in Circulation. Cardiovascular quality and outcomes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 150 papers, 11 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
150 citing papers in PubMed, 11 syntheses or guidelines pooled it.
- Patterns of multimorbidity, their outcomes and analytical methods for their determination: a systematic review.BMC public health · 2026Pooled it
- A systematic review and meta-analysis of disease clusters in multimorbidity.Nature communications · 2025Pooled 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
- Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Factors associated with the local control of brain metastases: a systematic search and machine learning application.BMC medical informatics and decision making · 2024Pooled it
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Prognostic models for clinical outcomes in patients with venous leg ulcers: A systematic review.Journal of vascular surgery. Venous and lymphatic disorders · 2024Pooled it
- The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem.Medical image analysis · 2021Pooled it
- Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis.Systematic reviews · 2021Pooled it
- Three-dimensional anatomical classification and machine learning prediction of the P6a-right hepatic vein relationship: surgical implications for laparoscopic right anterior sectionectomy.Surgical and radiologic anatomy : SRA · 2026Article
- Review
- Applying machine learning to pharmacovigilance data: A proof-of-concept study.British journal of clinical pharmacology · 2026Article
- Integrating Clinical Priorities into the Technical Validation of Machine Learning Classifiers: A Generalizable Framework Demonstrated on Opioid Misuse Screening.Journal of clinical medicine · 2026Article
- Reliability of the QUADAS-2 checklist in AI-based medical imaging: A comparison of human reviewers and ChatGPT-4o.Imaging science in dentistry · 2026Article
- Learning the Boundary Between Involvement and Severity: A Multi-Target Machine-Learning Analysis of Emotion Dysregulation, Impulsivity, Gambling Involvement, and Problem Severity in Psychiatric Outpatients.Clinical neuropsychiatry · 2026Article
- Evaluation Frameworks for Clinical AI Incorporating Validation Strategies, Real-World Applicability, and Ethical Principles: Scoping Review.Journal of medical Internet research · 2026Article
- Artificial stupidity or logimorphism? How misuse of language warps our thinking about 'artificial intelligence'.European heart journal. Digital health · 2026Article
- Physical Attractiveness and Chances of Being Invited to Interview With a Medical Residency Program: Retrospective Cohort Study.JMIR medical education · 2026Article
90 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses. Despite this popularity, many clinicians and researchers are not yet familiar with evaluating and interpreting ML analyses. Consequently, readers and peer-reviewers alike may either overestimate or underestimate the validity and credibility of an ML-based model. Conversely, ML experts without clinical experience may present details of the analysis that are too granular for a clinical readership to assess. Overwhelming evidence has shown poor reproducibility and reporting of ML models in clinical research suggesting the need for ML analyses to be presented in a clear, concise, and comprehensible manner to facilitate understanding and critical evaluation. We present a recommendation for transparent and structured reporting of ML analysis results specifically directed at clinical researchers. Furthermore, we provide a list of key reporting elements with examples that can be used as a template when preparing and submitting ML-based manuscripts for the same audience.
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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.