SynthesisJournal of clinical epidemiology2023
Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review.
Synthesis in Journal of clinical epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 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
22 citing papers in PubMed, 4 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Machine learning-based prediction model for postpartum stress urinary incontinence risk: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled 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
- A systematic review of cardiac surgery clinical prediction models that include intra-operative variables.Perfusion · 2025Pooled it
- A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality.PLoS medicine · 2025Pooled it
- Graph and Geometric Deep Learning for Intracranial Aneurysm Geometry and Hemodynamics: A Systematic Review with Implications for Neurovascular Implant Design and Evaluation.Life (Basel, Switzerland) · 2026Review
- Spin reporting is common in pilot and feasibility trials in hip and knee arthroplasty: a methodological analysis of a cross-sectional sample.Pilot and feasibility studies · 2026Article
- Craving for a Robust Methodology: A Systematic Review of Machine Learning Algorithms on Substance-Use Disorders Treatment Outcomes.International journal of mental health and addiction · 2026Article
- A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models - part 2: time-to-event outcomes.Diagnostic and prognostic research · 2025Article
- A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models for individual risk-part 1: binary outcomes.Diagnostic and prognostic research · 2025Article
- Predictive modeling for metastasis in oncology: current methods and future directions.Annals of medicine and surgery (2012) · 2025Review
- PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods.BMJ (Clinical research ed.) · 2025Article
- Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023).Journal of medical Internet research · 2025Article
- Clinical prediction models using machine learning in oncology: challenges and recommendations.BMJ oncology · 2025Review
- Article
- Ethical guidance for reporting and evaluating claims of AI outperforming human doctors.NPJ digital medicine · 2024Article
- Identifying and Minimizing Incentives for Competing Interests in Sports Medicine Publications.Sports medicine (Auckland, N.Z.) · 2024Article
- Identifying patients with undiagnosed small intestinal neuroendocrine tumours in primary care using statistical and machine learning: model development and validation study.British journal of cancer · 2024Article
- PET/CT deep learning prognosis for treatment decision support in esophageal squamous cell carcinoma.Insights into imaging · 2024Article
- Artificial Intelligence in Orthodontics: Critical Review.Journal of dental research · 2024Review
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.BMJ (Clinical research ed.) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 4 countries.
Funding
Abstract
objectivesIn biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction. STUDY DESIGN AND
settingWe conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices.
resultsWe included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion.
conclusionThe potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence.
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.