ReviewScience advances2024
REFORMS: Consensus-based Recommendations for Machine-learning-based Science.
Review in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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
38 citing papers in PubMed.
- The evaluation crisis in artificial intelligence-driven computational biology: Pitfalls and solutions.Quantitative biology (Beijing, China) · 2026Article
- Large Language Models for Clinical Data Extraction in Workplace Injury Rehabilitation: Protocol for a Retrospective Pilot Study of Accuracy, Fairness, and Methodological Considerations in Workers' Compensation Medical Chart Review.JMIR research protocols · 2026Article
- Artificial intelligence in toxicology: current advances, challenges and future directions.Archives of toxicology · 2026Review
- Data-driven interfacial regulations through molecular additive screening for batteries and electrocatalysis.Chemical science · 2026Review
- Photon-counting CT-enabled urolithiasis phenotyping: virtual non-iodine reconstructions, automated measurement, and spectral radiomics.Abdominal radiology (New York) · 2026Review
- Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences.Nature methods · 2026Review
- Evaluation Frameworks for Clinical AI Incorporating Validation Strategies, Real-World Applicability, and Ethical Principles: Scoping Review.Journal of medical Internet research · 2026Article
- Measuring complex constructs in large-scale text with computational social mixed methods.Behavior research methods · 2026Article
- Benchmarking deep learning architectures for MALDI-TOF mass spectrometry in infectious disease diagnostics and vector-borne disease surveillance.NPJ digital medicine · 2026Article
- Structured Inequality, Uncertain Lifespans: Demographic Perspectives on Predicting Individual-Level Longevity.Population and development review · 2026Article
- From Biosignals to Bedside: A Review of Real-Time Edge Machine Learning for Wearable Health Monitoring.Bioengineering (Basel, Switzerland) · 2026Review
- Pitfalls and risks of generative AI in machine learning.Patterns (New York, N.Y.) · 2026Review
- FAIR4prep: FAIR clinical informatics data preprocessing in artificial intelligence applications.Scientific data · 2026Article
- Article
- Post-transcriptional regulation of light-stress responses and predictive modeling in vegetableFrontiers in plant science · 2026Review
- Article
- Zoon0PredV: Potential Virus Species Crossover Prediction Using Convolutional Neural Networks and Viral Protein Sequence Patterns.Bioinformatics and biology insights · 2026Article
- Individualized Risk Stratification for Early Recurrence of Hepatocellular Carcinoma: A Clinical Tool Derived from a Large-Scale (n=3084) Cohort Study.Journal of hepatocellular carcinoma · 2026Article
- Evaluation framework for assessing the quality and safety of artificial intelligence solutions for the acute care surgeon: a narrative review from the Eastern Association for the Surgery of Trauma quality, safety, and outcomes committee.Trauma surgery & acute care open · 2026Review
- Multimodal landscape of atherosclerotic plaques: A spatial omics approach with mass spectrometry imaging.Analytica chimica acta · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear recommendations for conducting and reporting ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist (recommendations for machine-learning-based science). It consists of 32 questions and a paired set of guidelines. REFORMS was developed on the basis of a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility.
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.