SynthesisJCO clinical cancer informatics2019
Validation of a Semiautomated Natural Language Processing-Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance.
Synthesis in JCO clinical cancer informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Transforming evidence synthesis: A systematic review of the evolution of automated meta-analysis in the age of AI.Research synthesis methods · 2026Pooled it
- Adjusting for Ascertainment Bias in Meta-Analysis of Penetrance for Cancer Risk.Statistics in medicine · 2025Pooled it
- Bayesian meta-analysis of penetrance for cancer risk.Biometrics · 2024Pooled it
- Using meta-analysis and CNN-NLP to review and classify the medical literature for normal tissue complication probability in head and neck cancer.Radiation oncology (London, England) · 2024Pooled it
- Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes.JCO clinical cancer informatics · 2019Pooled it
- Meta-Analysis of Breast Cancer Risk for Individuals with PALB2 Pathogenic Variants.medRxiv : the preprint server for health sciences · 2024Article
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
- Search Behavior Regarding Cancer Susceptibility Genes Using a Clinical Decision Support Tool for Gene-Specific Penetrance: Content Analysis.JMIR cancer · 2021Article
- Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 2021Article
- Disease spectrum of gastric cancer susceptibility genes.Medical oncology (Northwood, London, England) · 2021Article
- Disease Spectrum of Breast Cancer Susceptibility Genes.Frontiers in oncology · 2021Article
- Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs.JAMIA open · 2020Article
- Intelligent, Autonomous Machines in Surgery.The Journal of surgical research · 2020Article
- Revolution of Medical Review: The Application of Meta-Analysis and Convolutional Neural Network-Natural Language Processing in Classifying the Literature for Head and Neck Cancer Radiotherapy.Cancer control : journal of the Moffitt Cancer CenterArticle
- Extracting Concepts for Precision Oncology from the Biomedical Literature.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
purposeQuantifying the risk of cancer associated with pathogenic mutations in germline cancer susceptibility genes-that is, penetrance-enables the personalization of preventive management strategies. Conducting a meta-analysis is the best way to obtain robust risk estimates. We have previously developed a natural language processing (NLP) -based abstract classifier which classifies abstracts as relevant to penetrance, prevalence of mutations, both, or neither. In this work, we evaluate the performance of this NLP-based procedure. MATERIALS AND
methodsWe compared the semiautomated NLP-based procedure, which involves automated abstract classification and text mining, followed by human review of identified studies, with the traditional procedure that requires human review of all studies. Ten high-quality gene-cancer penetrance meta-analyses spanning 16 gene-cancer associations were used as the gold standard by which to evaluate the performance of our procedure. For each meta-analysis, we evaluated the number of abstracts that required human review (workload) and the ability to identify the studies that were included by the authors in their quantitative analysis (coverage).
resultsCompared with the traditional procedure, the semiautomated NLP-based procedure led to a lower workload across all 10 meta-analyses, with an overall 84% reduction (2,774 abstracts
conclusionWe demonstrated that an NLP-based procedure can significantly reduce the review workload without compromising the ability to identify relevant studies. NLP algorithms have promising potential for reducing human efforts in the literature review process.
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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.